Image_Enhancement
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TGIF2: Extended Text-Guided Inpainting Forgery Dataset & Benchmark
2026-03-30 15:59:16Hannes Mareen, Dimitrios Karageorgiou, Paschalis Giakoumoglou, Peter Lambert, Symeon Papadopoulos, Glenn Van WallendaelAbstract
Generative AI has made text-guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text-guided inpainting forgery (TGIF) dataset, show that image forgery localization (IFL) methods can localize manipulations in spliced images but struggle not in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect fully regenerated images but cannot perform localization. With new generative inpainting models emerging and the open problem of localization in FR images remaining, updated datasets and benchmarks are needed. We introduce TGIF2, an extended version of TGIF, that captures recent advances in text-guided inpainting and enables a deeper analysis of forensic robustness. TGIF2 augments the original dataset with edits generated by FLUX.1 models, as well as with random non-semantic masks. Using the TGIF2 dataset, we conduct a forensic evaluation spanning IFL and SID, including fine-tuning IFL methods on FR images and generative super-resolution attacks. Our experiments show that both IFL and SID methods degrade on FLUX.1 manipulations, highlighting limited generalization. Additionally, while fine-tuning improves localization on FR images, evaluation with random non-semantic masks reveals object bias. Furthermore, generative super-resolution significantly weakens forensic traces, demonstrating that common image enhancement operations can undermine current forensic pipelines. In summary, TGIF2 provides an updated dataset and benchmark, which enables new insights into the challenges posed by modern inpainting and AI-based image enhancements. TGIF2 is available at this https URL.
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URL
https://arxiv.org/abs/2603.28613
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4DRaL: Bridging 4D Radar with LiDAR for Place Recognition using Knowledge Distillation
2026-03-27 09:28:51Ningyuan Huang, Zhiheng Li, Zheng FangAbstract
Place recognition is crucial for loop closure detection and global localization in robotics. Although mainstream algorithms typically rely on cameras and LiDAR, these sensors are susceptible to adverse weather conditions. Fortunately, the recently developed 4D millimeter-wave radar (4D radar) offers a promising solution for all-weather place recognition. However, the inherent noise and sparsity in 4D radar data significantly limit its performance. Thus, in this paper, we propose a novel framework called 4DRaL that leverages knowledge distillation (KD) to enhance the place recognition performance of 4D radar. Its core is to adopt a high-performance LiDAR-to-LiDAR (L2L) place recognition model as a teacher to guide the training of a 4D radar-to-4D radar (R2R) place recognition model. 4DRaL comprises three key KD modules: a local image enhancement module to handle the sparsity of raw 4D radar points, a feature distribution distillation module that ensures the student model generates more discriminative features, and a response distillation module to maintain consistency in feature space between the teacher and student models. More importantly, 4DRaL can also be trained for 4D radar-to-LiDAR (R2L) place recognition through different module configurations. Experimental results prove that 4DRaL achieves state-of-the-art performance in both R2R and R2L tasks regardless of normal or adverse weather.
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URL
https://arxiv.org/abs/2603.26206
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Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework
2026-03-26 10:40:08Hongru Han, Tingrui Guo, Liming Zhang, Yan Su, Qiwen Xu, Zhuohua YeAbstract
Low-light image enhancement (LLIE) has traditionally been formulated as a deterministic mapping. However, this paradigm often struggles to account for the ill-posed nature of the task, where unknown ambient conditions and sensor parameters create a multimodal solution space. Consequently, state-of-the-art methods frequently encounter luminance discrepancies between predictions and labels, often necessitating "gt-mean" post-processing to align output luminance for evaluation. To address this fundamental limitation, we propose a transition toward Controllable Low-light Enhancement (CLE), explicitly reformulating the task as a well-posed conditional problem. To this end, we introduce CLE-RWKV, a holistic framework supported by Light100, a new benchmark featuring continuous real-world illumination transitions. To resolve the conflict between luminance control and chromatic fidelity, a noise-decoupled supervision strategy in the HVI color space is employed, effectively separating illumination modulation from texture restoration. Architecturally, to adapt efficient State Space Models (SSMs) for dense prediction, we leverage a Space-to-Depth (S2D) strategy. By folding spatial neighborhoods into channel dimensions, this design allows the model to recover local inductive biases and effectively bridge the "scanning gap" inherent in flattened visual sequences without sacrificing linear complexity. Experiments across seven benchmarks demonstrate that our approach achieves competitive performance and robust controllability, providing a real-world multi-illumination alternative that significantly reduces the reliance on gt-mean post-processing.
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URL
https://arxiv.org/abs/2603.25296
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HSI Image Enhancement Classification Based on Knowledge Distillation: A Study on Forgetting
2026-03-19 02:03:51Songfeng ZhuAbstract
In incremental classification tasks for hyperspectral images, catastrophic forgetting is an unavoidable challenge. While memory recall methods can mitigate this issue, they heavily rely on samples from old categories. This paper proposes a teacher-based knowledge retention method for incremental image classification. It alleviates model forgetting of old category samples by utilizing incremental category samples, without depending on old category samples. Additionally, this paper introduces a mask-based partial category knowledge distillation algorithm. By decoupling knowledge distillation, this approach filters out potentially misleading information that could misguide the student model, thereby enhancing overall accuracy. Comparative and ablation experiments demonstrate the proposed method's robust performance.
Abstract (translated)
在高光谱图像增量分类任务中，灾难性遗忘是一个不可避免的挑战。尽管记忆召回方法可以缓解此问题，但它们严重依赖旧类别的样本。本文提出了一种基于教师的知识保留方法用于增量图像分类，该方法通过利用增量类别样本来减轻模型对旧类别样本的遗忘，且不依赖旧类别样本。此外，本文引入了一种基于掩码的局部类别知识蒸馏算法，通过解耦知识蒸馏，该方法能够过滤可能误导学生模型的潜在错误信息，从而提升整体准确率。对比实验与消融实验均验证了所提方法的稳健性能。
URL
https://arxiv.org/abs/2603.20292
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DST-Net: A Dual-Stream Transformer with Illumination-Independent Feature Guidance and Multi-Scale Spatial Convolution for Low-Light Image Enhancement
2026-03-17 13:07:32Yicui Shi, Yuhan Chen, Xiangfei Huang, Zhenguo Wang, Wenxuan Yu, Ying FangAbstract
Low-light image enhancement aims to restore the visibility of images captured by visual sensors in dim environments by addressing their inherent signal degradations, such as luminance attenuation and structural corruption. Although numerous algorithms attempt to improve image quality, existing methods often cause a severe loss of intrinsic signal priors. To overcome these challenges, we propose a Dual-Stream Transformer Network (DST-Net) based on illumination-agnostic signal prior guidance and multi-scale spatial convolutions. First, to address the loss of critical signal features under low-light conditions, we design a feature extraction module. This module integrates Difference of Gaussians (DoG), LAB color space transformations, and VGG-16 for texture extraction, utilizing decoupled illumination-agnostic features as signal priors to continuously guide the enhancement process. Second, we construct a dual-stream interaction architecture. By employing a cross-modal attention mechanism, the network leverages the extracted priors to dynamically rectify the deteriorated signal representation of the enhanced image, ultimately achieving iterative enhancement through differentiable curve estimation. Furthermore, to overcome the inability of existing methods to preserve fine structures and textures, we propose a Multi-Scale Spatial Fusion Block (MSFB) featuring pseudo-3D and 3D gradient operator convolutions. This module integrates explicit gradient operators to recover high-frequency edges while capturing inter-channel spatial correlations via multi-scale spatial convolutions. Extensive evaluations and ablation studies demonstrate that DST-Net achieves superior performance in subjective visual quality and objective metrics. Specifically, our method achieves a PSNR of 25.64 dB on the LOL dataset. Subsequent validation on the LSRW dataset further confirms its robust cross-scene generalization.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16482
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Advancing Visual Reliability: Color-Accurate Underwater Image Enhancement for Real-Time Underwater Missions
2026-03-17 10:51:00Yiqiang Zhou, Yifan Chen, Zhe Sun, Jijun Lu, Ye Zheng, Xuelong LiAbstract
Underwater image enhancement plays a crucial role in providing reliable visual information for underwater platforms, since strong absorption and scattering in water-related environments generally lead to image quality degradation. Existing high-performance methods often rely on complex architectures, which hinder deployment on underwater devices. Lightweight methods often sacrifice quality for speed and struggle to handle severely degraded underwater images. To address this limitation, we present a real-time underwater image enhancement framework with accurate color restoration. First, an Adaptive Weighted Channel Compensation module is introduced to achieve dynamic color recovery of the red and blue channels using the green channel as a reference anchor. Second, we design a Multi-branch Re-parameterized Dilated Convolution that employs multi-branch fusion during training and structural re-parameterization during inference, enabling large receptive field representation with low computational overhead. Finally, a Statistical Global Color Adjustment module is employed to optimize overall color performance based on statistical priors. Extensive experiments on eight datasets demonstrate that the proposed method achieves state-of-the-art performance across seven evaluation metrics. The model contains only 3,880 inference parameters and achieves an inference speed of 409 FPS. Our method improves the UCIQE score by 29.7% under diverse environmental conditions, and the deployment on ROV platforms and performance gains in downstream tasks further validate its superiority for real-time underwater missions.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16363
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Anchor then Polish for Low-light Enhancement
2026-03-16 16:06:30Tianle Du, Mingjia Li, Hainuo Wang, Xiaojie GuoAbstract
Low-light image enhancement is challenging due to entangled degradations, mainly including poor illumination, color shifts, and texture interference. Existing methods often rely on complex architectures to address these issues jointly but may overfit simple physical constraints, leading to global distortions. This work proposes a novel anchor-then-polish (ATP) framework to fundamentally decouple global energy alignment from local detail refinement. First, macro anchoring is customized to (greatly) stabilize luminance distribution and correct color by learning a scene-adaptive projection matrix with merely 12 degrees of freedom, revealing that a simple linear operator can effectively align global energy. The macro anchoring then reduces the task to micro polishing, which further refines details in the wavelet domain and chrominance space under matrix guidance. A constrained luminance update strategy is designed to ensure global consistency while directing the network to concentrate on fine-grained polishing. Extensive experiments on multiple benchmarks show that our method achieves state-of-the-art performance, producing visually natural and quantitatively superior low-light enhancements.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15472
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Low-light Image Enhancement with Retinex Decomposition in Latent Space
2026-03-16 11:24:35Bolun Zheng, Qingshan Lei, Quan Chen, Qianyu Zhang, Kainan Yu, Xu Jia, Lingyu ZhuAbstract
Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components. To address this, we propose a Retinex-Guided Transformer~(RGT) model, which is a two-stage model consisting of decomposition and enhancement phases. First, we propose a latent space decomposition strategy to separate reflectance and illumination components. By incorporating the log transformation and 1-pixel offset, we convert the intrinsically multiplicative relationship into an additive formulation, enhancing decomposition stability and precision. Subsequently, we construct a U-shaped component refiner incorporating the proposed guidance fusion transformer block. The component refiner refines reflectance component to preserve texture details and optimize illumination distribution, effectively transforming low-light inputs to normal-light counterparts. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15131
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A Physically-Grounded Attack and Adaptive Defense Framework for Real-World Low-Light Image Enhancement
2026-03-15 09:44:54Tongshun Zhang, Pingping Liu, Yuqing Lei, Zixuan Zhong, Qiuzhan Zhou, Zhiyuan ZhaAbstract
Limited illumination often causes severe physical noise and detail degradation in images. Existing Low-Light Image Enhancement (LLIE) methods frequently treat the enhancement process as a blind black-box mapping, overlooking the physical noise transformation during imaging, leading to suboptimal performance. To address this, we propose a novel LLIE approach, conceptually formulated as a physics-based attack and display-adaptive defense paradigm. Specifically, on the attack side, we establish a physics-based Degradation Synthesis (PDS) pipeline. Unlike standard data augmentation, PDS explicitly models Image Signal Processor (ISP) inversion to the RAW domain, injects physically plausible photon and read noise, and re-projects the data to the sRGB domain. This generates high-fidelity training pairs with explicitly parameterized degradation vectors, effectively simulating realistic attacks on clean signals. On the defense side, we construct a dual-layer fortified system. A noise predictor estimates degradation parameters from the input sRGB image. These estimates guide a degradation-aware Mixture of Experts (DA-MoE), which dynamically routes features to experts specialized in handling specific noise intensities. Furthermore, we introduce an Adaptive Metric Defense (AMD) mechanism, dynamically calibrating the feature embedding space based on noise severity, ensuring robust representation learning under severe degradation. Extensive experiments demonstrate that our approach offers significant plug-and-play performance enhancement for existing benchmark LLIE methods, effectively suppressing real-world noise while preserving structural fidelity. The sourced code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14304
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Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
2026-03-14 21:25:46Daniel Tweneboah Anyimadu (Department of Computer Science, University of Exeter, United Kingdom), Mohammed Abdalla (Neurology Department, Royal Devon and Exeter Hospital, Exeter, United Kingdom), Mohammed M. Abdelsamea (Department of Computer Science, University of Exeter, United Kingdom), Ahmed Karam Eldaly (Department of Computer Science, University of Exeter, United Kingdom, UCL Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom)Abstract
Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14125
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Empowering Semantic-Sensitive Underwater Image Enhancement with VLM
2026-03-13 08:17:06Guodong Fan, Shengning Zhou, Genji Yuan, Huiyu Li, Jingchun Zhou, Jinjiang LiAbstract
In recent years, learning-based underwater image enhancement (UIE) techniques have rapidly evolved. However, distribution shifts between high-quality enhanced outputs and natural images can hinder semantic cue extraction for downstream vision tasks, thereby limiting the adaptability of existing enhancement models. To address this challenge, this work proposes a new learning mechanism that leverages Vision-Language Models (VLMs) to empower UIE models with semantic-sensitive capabilities. To be concrete, our strategy first generates textual descriptions of key objects from a degraded image via VLMs. Subsequently, a text-image alignment model remaps these relevant descriptions back onto the image to produce a spatial semantic guidance map. This map then steers the UIE network through a dual-guidance mechanism, which combines cross-attention and an explicit alignment loss. This forces the network to focus its restorative power on semantic-sensitive regions during image reconstruction, rather than pursuing a globally uniform improvement, thereby ensuring the faithful restoration of key object features. Experiments confirm that when our strategy is applied to different UIE baselines, significantly boosts their performance on perceptual quality metrics as well as enhances their performance on detection and segmentation tasks, validating its effectiveness and adaptability.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12773
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Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments
2026-03-12 14:00:20Pankaj Deoli, Karthik Ranganath, Karsten BernsAbstract
RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.
Abstract (translated)
RGB-NIR（红绿蓝-近红外）图像配准在传感器融合、图像增强和非公路自主导航中扮演着重要角色。在这项工作中，我们评估了传统的基于深度学习（DL）的图像配准技术，以考察它们是否适用于非公路林业应用。NeMAR 在六种不同配置下训练后显示出部分成功，然而其生成对抗网络（GAN）损失不稳定的问题表明在保持几何一致性方面存在挑战。当 MURF 被测试用于非公路林业数据时，在共享信息提取期间显示出了大规模特征对齐的前景，但在密集植被中的细节处理上遇到了困难。尽管这只是初步评估，我们的研究仍需要进一步改进以实现适用于非公路森林应用的强大、多尺度配准技术。
URL
https://arxiv.org/abs/2603.11952
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VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement
2026-03-10 15:10:44Zhixin Cheng, Fangwen Zhang, Xiaotian Yin, Baoqun Yin, Haodian WangAbstract
Most sRGB-based LLIE methods suffer from entangled luminance and color, while the HSV color space offers insufficient decoupling at the cost of introducing significant red and black noise artifacts. Recently, the HVI color space has been proposed to address these limitations by enhancing color fidelity through chrominance polarization and intensity compression. However, existing methods could suffer from channel-level inconsistency between luminance and chrominance, and misaligned color distribution may lead to unnatural enhancement results. To address these challenges, we propose the Variance-Driven Channel Recalibration for Robust Low-Light Enhancement (VCR), a novel framework for low-light image enhancement. VCR consists of two main components, including the Channel Adaptive Adjustment (CAA) module, which employs variance-guided feature filtering to enhance the model's focus on regions with high intensity and color distribution. And the Color Distribution Alignment (CDA) module, which enforces distribution alignment in the color feature space. These designs enhance perceptual quality under low-light conditions. Experimental results on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance compared with existing methods.
Abstract (translated)
大多数基于sRGB的低光图像增强（LLIE）方法都存在亮度和色彩相互纠缠的问题，而HSV颜色空间虽然在一定程度上可以分离这两者，但会引入显著的红色和黑色噪声。最近提出了HVI颜色空间来解决这些问题，通过色度极化和强度压缩提高色彩保真度。然而，现有方法可能会因亮度通道与色度通道之间的一致性问题而受到影响，导致不自然的增强效果。为了解决这些挑战，我们提出了一种新的低光图像增强框架——基于方差驱动通道校准的稳健低光增强（VCR）。该框架包含两个主要组件：通道自适应调整（CAA）模块和颜色分布对齐（CDA）模块。CAA模块采用方差引导的特征过滤来提升模型在高亮度区域与色彩分布上的关注；而CDA模块则确保颜色特征空间中的分布一致性。这些设计提高了低光条件下的感知质量。实验结果表明，在多个基准数据集上，所提出的方法相较于现有方法达到了最先进的性能水平。
URL
https://arxiv.org/abs/2603.10975
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IAML: Illumination-Aware Mirror Loss for Progressive Learning in Low-Light Image Enhancement Auto-encoders
2026-03-09 10:48:55Farida Mohsen, Tala Zaim, Ali Al-Zawqari, Ali Safa, Samir BelhaouariAbstract
This letter presents a novel training approach and loss function for learning low-light image enhancement auto-encoders. Our approach revolves around the use of a teacher-student auto-encoder setup coupled to a progressive learning approach where multi-scale information from clean image decoder feature maps is distilled into each layer of the student decoder in a mirrored fashion using a newly-proposed loss function termed Illumination-Aware Mirror Loss (IAML). IAML helps aligning the feature maps within the student decoder network with clean feature maps originating from the teacher side while taking into account the effect of lighting variations within the input images. Extensive benchmarking of our proposed approach on three popular low-light image enhancement datasets demonstrate that our model achieves state-of-the-art performance in terms of average SSIM, PSNR and LPIPS reconstruction accuracy metrics. Finally, ablation studies are performed to clearly demonstrate the effect of IAML on the image reconstruction accuracy.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13363
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Retinex Meets Language: A Physics-Semantics-Guided Underwater Image Enhancement Network
2026-03-07 07:10:20Shixuan Xu, Yabo Liu, Junyu Dong, Xinghui DongAbstract
Underwater images often suffer from severe degradation caused by light absorption and scattering, leading to color distortion, low contrast and reduced visibility. Existing Underwater Image Enhancement (UIE) methods can be divided into two categories, i.e., prior-based and learning-based methods. The former rely on rigid physical assumptions that limit the adaptability, while the latter often face data scarcity and weak generalization. To address these issues, we propose a Physics-Semantics-Guided Underwater Image Enhancement Network (PSG-UIENet), which couples the Retinex-grounded illumination correction with the language-informed guidance. This network comprises a Prior-Free Illumination Estimator, a Cross-Modal Text Aligner and a Semantics-Guided Image Restorer. In particular, the restorer leverages the textual descriptions generated by the Contrastive Language-Image Pre-training (CLIP) model to inject high-level semantics for perceptually meaningful guidance. Since multimodal UIE data sets are not publicly available, we also construct a large-scale image-text UIE data set, namely, LUIQD-TD, which contains 6,418 image-reference-text triplets. To explicitly measure and optimize semantic consistency between textual descriptions and images, we further design an Image-Text Semantic Similarity (ITSS) loss function. To our knowledge, this study makes the first effort to introduce both textual guidance and the multimodal data set into UIE tasks. Extensive experiments on our data set and four publicly available data sets demonstrate that the proposed PSG-UIENet achieves superior or comparable performance against fifteen state-of-the-art methods.
Abstract (translated)
水下图像往往因光吸收和散射而遭受严重退化，导致色彩失真、对比度低以及能见度降低。现有的水下图像增强（UIE）方法可以分为基于先验知识的方法和基于学习的方法两大类。前者依赖于刚性的物理假设，限制了其适应性；后者则通常面临数据稀缺及泛化能力弱的问题。为了解决这些问题，我们提出了一种由物理-语义引导的水下图像增强网络（PSG-UIENet），该网络结合了基于Retinex原理的光照校正与语言信息指导。这个网络包括一个无先验假设的光照估计器、一个多模态文本对齐器以及一种受语义指导的图像恢复器。特别是，恢复器利用由对比性语言图像预训练（CLIP）模型生成的文字描述来注入高层次的语义信息，提供感知上有意义的引导。由于多模态UIE数据集不公开可用，我们也构建了一个大规模的图文UIE数据集，称为LUIQD-TD，包含6,418个图像-参考-文本三元组。为了明确地测量和优化文字描述与图像之间的语义一致性，我们进一步设计了一种图像-文本语义相似性（ITSS）损失函数。据我们所知，这项研究首次将文本指导和多模态数据集引入到UIE任务中。在我们的数据集以及四个公开可用的数据集上进行的大量实验表明，提出的PSG-UIENet与现有的十五种最先进的方法相比，在性能上表现出优越或相当的表现。
URL
https://arxiv.org/abs/2603.07076
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BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement
2026-03-05 09:14:10Zishu Yao, Xiang-Xiang Su, Shengning Zhou, Guang-Yong Chen, Guodong Fan, Xing ChenAbstract
Event cameras, with their high dynamic range, show great promise for Low-light Image Enhancement (LLIE). Existing works primarily focus on designing effective modal fusion strategies. However, a key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low signal-to-noise ratio (SNR) in images, which causes severe noise coupling during modal fusion, creating a critical performance bottleneck. We therefore posit that precise event denoising is the prerequisite to unlocking the full potential of event-based fusion. To this end, we propose BiEvLight, a hierarchical and task-aware framework that collaboratively optimizes enhancement and denoising by exploiting their intrinsic interdependence. Specifically, BiEvLight exploits the strong gradient correlation between images and events to build a gradient-guided event denoising prior that alleviates insufficient denoising in heavily noisy regions. Moreover, instead of treating event denoising as a static pre-processing stage-which inevitably incurs a trade-off between over- and under-denoising and cannot adapt to the requirements of a specific enhancement objective-we recast it as a bilevel optimization problem constrained by the enhancement task. Through cross-task interaction, the upper-level denoising problem learns event representations tailored to the lower-level enhancement objective, thereby substantially improving overall enhancement quality. Extensive experiments on the Real-world noise Dataset SDE demonstrate that our method significantly outperforms state-of-the-art (SOTA) approaches, with average improvements of 1.30dB in PSNR, 2.03dB in PSNR* and 0.047 in SSIM, respectively. The code will be publicly available at this https URL.
Abstract (translated)
事件相机因其高动态范围，在低光图像增强（LLIE）方面展现出巨大潜力。现有研究主要集中在设计有效的模态融合策略上，然而一个关键挑战是事件中存在的固有背景活动（BA）噪声和图像中的低信噪比（SNR），这导致在模态融合过程中产生严重的噪声耦合现象，形成性能瓶颈的关键问题。因此，我们认为精确的事件去噪是解锁基于事件融合潜力的前提条件。为此，我们提出了BiEvLight，这是一个分层且任务感知的框架，通过利用其内在相关性来协同优化图像增强和去噪过程。 具体来说，BiEvLight 利用图像与事件之间的强梯度关联构建了一个引导式事件去噪先验模型，从而缓解了在高噪声区域中因去噪不足而产生的问题。此外，我们没有将事件去噪视为一个静态预处理阶段——这不可避免地会导致过度去噪和欠去噪之间的权衡，并且无法适应特定增强目标的需求——而是将其重新构造成一个受增强任务约束的双层优化问题。通过跨任务交互，上层去噪问题学习到了针对下层增强目标所需的事件表示形式，从而大幅提高了整体增强质量。 在真实噪声数据集SDE上的广泛实验表明，我们的方法显著优于现有最先进的（SOTA）方法，在PSNR、PSNR*和SSIM方面分别平均提升了1.30dB、2.03dB 和 0.047。代码将公开发布在此链接：[https://github.com/your-repo-url](https://github.com/your-repo-url)（请确保替换为实际的GitHub存储库URL）。
URL
https://arxiv.org/abs/2603.04975
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Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design
2026-03-02 11:50:09Bosen Lin, Feng Gao, Yanwei Yu, Junyu Dong, Qian DuAbstract
In real underwater environments, downstream image recognition tasks such as semantic segmentation and object detection often face challenges posed by problems like blurring and color inconsistencies. Underwater image enhancement (UIE) has emerged as a promising preprocessing approach, aiming to improve the recognizability of targets in underwater images. However, most existing UIE methods mainly focus on enhancing images for human visual perception, frequently failing to reconstruct high-frequency details that are critical for task-specific recognition. To address this issue, we propose a Downstream Task-Inspired Underwater Image Enhancement (DTI-UIE) framework, which leverages human visual perception model to enhance images effectively for underwater vision tasks. Specifically, we design an efficient two-branch network with task-aware attention module for feature mixing. The network benefits from a multi-stage training framework and a task-driven perceptual loss. Additionally, inspired by human perception, we automatically construct a Task-Inspired UIE Dataset (TI-UIED) using various task-specific networks. Experimental results demonstrate that DTI-UIE significantly improves task performance by generating preprocessed images that are beneficial for downstream tasks such as semantic segmentation, object detection, and instance segmentation. The codes are publicly available at this https URL.
Abstract (translated)
在真实的水下环境中，下游图像识别任务（如语义分割和目标检测）常常会遇到诸如模糊和颜色不一致等问题所带来的挑战。水下图像增强（UIE）作为一种前景预处理方法应运而生，旨在提高水下图像中目标的可辨识性。然而，大多数现有的UIE方法主要集中在改善人类视觉感知效果上，通常无法恢复对特定任务识别至关重要的高频细节信息。为了解决这一问题，我们提出了一种受下游任务启发的水下图像增强（DTI-UIE）框架，该框架利用了人类视觉感知模型来有效提高水下视觉任务中的图像质量。具体而言，我们设计了一个高效的双分支网络，并配备了具备任务意识注意模块的功能混合结构。此外，该网络受益于一个多阶段训练框架以及一种由任务驱动的感知损失函数。另外，受人类感知启发，我们利用各种特定任务的网络自动构建了“任务启发式水下图像增强数据集”（TI-UIED）。实验结果表明，DTI-UIE通过生成有利于下游任务如语义分割、目标检测和实例分割等任务处理的预处理图像，显著提升了这些任务的表现。相关代码可以在以下链接获得：[此URL]。
URL
https://arxiv.org/abs/2603.01767
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Neural Discrimination-Prompted Transformers for Efficient UHD Image Restoration and Enhancement
2026-03-01 00:51:32Cong Wang, Jinshan Pan, Liyan Wang, Wei Wang, Yang YangAbstract
We propose a simple yet effective UHDPromer, a neural discrimination-prompted Transformer, for Ultra-High-Definition (UHD) image restoration and enhancement. Our UHDPromer is inspired by an interesting observation that there implicitly exist neural differences between high-resolution and low-resolution features, and exploring such differences can facilitate low-resolution feature representation. To this end, we first introduce Neural Discrimination Priors (NDP) to measure the differences and then integrate NDP into the proposed Neural Discrimination-Prompted Attention (NDPA) and Neural Discrimination-Prompted Network (NDPN). The proposed NDPA re-formulates the attention by incorporating NDP to globally perceive useful discrimination information, while the NDPN explores a continuous gating mechanism guided by NDP to selectively permit the passage of beneficial content. To enhance the quality of restored images, we propose a super-resolution-guided reconstruction approach, which is guided by super-resolving low-resolution features to facilitate final UHD image restoration. Experiments show that UHDPromer achieves the best computational efficiency while still maintaining state-of-the-art performance on $3$ UHD image restoration and enhancement tasks, including low-light image enhancement, image dehazing, and image deblurring. The source codes and pre-trained models will be made available at this https URL.
Abstract (translated)
我们提出了一种简单而有效的UHDPromer，这是一种神经区分提示Transformer，用于超高清（UHD）图像的恢复和增强。我们的UHDPromer受到一个有趣观察的启发，即高分辨率特征与低分辨率特征之间隐含地存在神经差异，并且探索这些差异可以促进低分辨率特征的表现形式。为此，我们首先引入了神经区分先验（NDP），用于衡量这些差异，然后将NDP整合到提出的神经区分提示注意机制（NDPA）和神经区分提示网络（NDPN）中。所提出的NDPA通过融合NDP重新定义注意力机制，以全局感知有用的区分信息，而NDPN则探索一种由NDP指导的连续门控机制，以便选择性地允许有益内容通过。为了提升恢复图像的质量，我们提出了一种超分辨率引导重建方法，该方法受低分辨率特征超解析过程的引导，从而促进最终的UHD图像恢复。 实验结果表明，UHDPromer在计算效率上表现出最佳水平，并且仍能在三个UHD图像恢复和增强任务（包括低光图像增强、去雾化和去除模糊）中保持最先进的性能。源代码及预训练模型可在上述网址获取。
URL
https://arxiv.org/abs/2603.00853
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Direct low-field MRI super-resolution using undersampled k-space
2026-02-28 14:23:31Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam EldalyAbstract
Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.
Abstract (translated)
低场磁共振成像（MRI）提供了经济实惠的诊断影像访问途径，但存在采集时间长和图像质量有限的问题。通过k空间欠采样可以实现加速成像，而超分辨率（SR）和图像质量转换（IQT）方法通常依赖于空间域后的处理步骤。在本研究中，我们提出了一种新的框架，直接从欠采样的低场k空间重构高场MR类似的图像。我们的方法采用了一个k空间双通道U-Net架构，该架构能够处理欠采样k空间的实部和虚部组件，以恢复缺失的频率内容。 实验结果表明，在低场脑MRI数据上的应用中，我们提出的基于k空间的图像增强方法在性能上显著优于传统的空间域方法。此外，从欠采样的k空间重构出的图像质量可以与完全采样的k空间采集得到的质量相媲美。据我们所知，这是第一个直接从欠采样k空间研究低场MRI超分辨率和图像质量转换的工作。
URL
https://arxiv.org/abs/2603.00668
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Diffusion-Based Low-Light Image Enhancement with Color and Luminance Priors
2026-02-27 22:15:27Xuanshuo Fu, Lei Kang, Javier Vazquez-CorralAbstract
Low-light images often suffer from low contrast, noise, and color distortion, degrading visual quality and impairing downstream vision tasks. We propose a novel conditional diffusion framework for low-light image enhancement that incorporates a Structured Control Embedding Module (SCEM). SCEM decomposes a low-light image into four informative components including illumination, illumination-invariant features, shadow priors, and color-invariant cues. These components serve as control signals that condition a U-Net-based diffusion model trained with a simplified noise-prediction loss. Thus, the proposed SCEM equipped Diffusion method enforces structured enhancement guided by physical priors. In experiments, our model is trained only on the LOLv1 dataset and evaluated without fine-tuning on LOLv2-real, LSRW, DICM, MEF, and LIME. The method achieves state-of-the-art performance in quantitative and perceptual metrics, demonstrating strong generalization across benchmarks. this https URL.
Abstract (translated)
低光图像常常会因为对比度低、噪声多和色彩失真等问题而降低视觉质量，影响下游的视觉任务。我们提出了一种新的条件扩散框架来增强低光图像，并在此框架中引入了一个结构化控制嵌入模块（SCEM）。SCEM 将一幅低光图像分解成四个信息量丰富的组成部分：照明、非光照不变特征、阴影先验和色彩不变线索。这些部分作为控制信号，用于调节一个基于 U-Net 的扩散模型的训练过程，该模型使用简化的噪声预测损失进行训练。因此，所提出的 SCEM 配备的方法通过物理先验指导来执行结构化增强。 在实验中，我们的模型仅在 LOLv1 数据集上进行了训练，并且在没有微调的情况下对 LOLv2-real、LSRW、DICM、MEF 和 LIME 进行了评估。该方法在定量和感知指标上都取得了最先进的性能，在不同基准测试中的泛化能力表现出色。 原文链接：[请在此处插入实际的URL链接，上述描述并未提供具体的链接地址]
URL
https://arxiv.org/abs/2603.00337
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