Image Enhancement Techniques

Neueste Paper

• ExpandDiff: Dynamic Range Expanding Diffusion for Single-Image HDR Reconstruction

  Mehmet Emre and{\i}ran, Zhuoqian Yang, Liying Lu, Mathieu Salzmann, Sabine S\"usstrunk · 1. Oktober 2026

  Single-image HDR reconstruction requires inferring missing detail while preserving the visible content of an LDR image. Differences in sensor dynamic range and exposure cause LDR images to lose varying amounts of information in shadows and highlights. We present ExpandDiff, a conditional diffusion p…

• Lens Flare Removal and Reconstruction

  Tarun Yenamandra, Jonathon Luiten, Daniel Cremers, Nathan Matsuda · 1. Oktober 2026

  The presence of lens flares in images can significantly reduce the quality of downstream application results for tasks such as 3D scene reconstruction. This is because lens flares are a property of the camera imaging system, and not a part of the underlying scene being modeled. There are previous me…

• After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data

  Shilin Hu, Jingyi Xu, Dimitris Samaras, Hieu Le · 1. Oktober 2026

  Shadow removal looks nearly solved on established benchmarks, yet remains brittle in the real world. Models have advanced; the paired training data they rely on have barely changed in nearly a decade. The reason is simple: obtaining a shadow-free target requires removing the occluder while keeping t…

• Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques

  Alexandra Malyugina, Yini Li, Joanne Lin, Nantheera Anantrasirichai · 28. September 2026

  Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the performance of a wide range of downstream computer vision tasks. This survey presents a comprehensive review of video restoration and enhancement techniques w…

• Double-stream registration with pyramid fusion for HDR video with alternating exposures

  Onofre Martorell, Ivan Pereira-S\'anchez, Antoni Fuentes, Antoni Buades · 28. September 2026

  High dynamic range (HDR) video reconstruction from al\-ter\-na\-ting-exposure sequences remains challenging, especially in regions with extreme luminance variation. We propose a novel HDR reconstruction framework based on dual-stream registration and accurate pyramid fusion. Given three consecutive …

• IDM-Net: A Lightweight Illumination-Decoupled Modulation Network for Low-Light Image Enhancement

  Cheng-Yen Hsiao, Jing-Ming Guo · 28. September 2026

  Low-light image enhancement (LLIE) remains challenging for lightweight models because illumination restoration and color fidelity are difficult to optimize simultaneously in the RGB color space. Although recent color-decoupled methods separate luminance and chrominance representations, they primaril…

• LLPR: Location-aware learning and physics-based reconstruction for raindrop removal from a single image

  Zewei He, Xingyu Liu, Xing Luo, Guizhong Fu, Zixuan Chen, Yu Chen, Jinlei Li, Zhe-Ming Lu · 28. September 2026

  Raindrops can cause occlusion and distortion in the background scenes due to their adherence to windows or camera lenses. Existing raindrop removal methods concentrate on designing sophisticated CNN or Transformer architectures to recover distorted and missing texture. In this paper, we try to integ…

• SEE Challenge 2026: Event-Guided Brightness Adjustment Across a Broad Illumination Range

  Yunfan Lu, Mingchao Xu, Hanyu Zhou, Shaoyu Liu, Haoyue Liu, Peiqi Duan, Shihan Peng, Yinqiang Zheng, Boxin Shi, Gim Hee Lee, Hui Xiong, Davide Scaramuzza · 25. September 2026

  Event cameras provide a high dynamic range and preserve brightness-change cues in lighting conditions where conventional RGB frames may be noisy or saturated. To benchmark event-guided restoration across a broad illumination range, we organized the SEE Challenge 2026 with the Event-Based Multimodal …

• HaRP: High Dynamic Range Photosequencing through Dual Reversed Shutter Scanning

  Xiang Ji, Guixu Lin, Jiancheng Zhao, Zhengwei Yin, Yinqiang Zheng · 24. September 2026

  The adoption of CMOS sensors in mobile photography is frequently compromised by the rolling shutter (RS) effect, which introduces geometric distortions and motion artifacts. Particularly, recent rolling shutter with global reset (RSGR) mode, while mitigating some RS issues, also incurs major limitat…

• Breaking Weather-Content Coupling: Type-Severity Guided Progressive Disentanglement for All-in-One Infrared Restoration

  Xinyao Wang, Lijun He, Zhihan Ren, Fan Li · 24. September 2026

  Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake structural responses that are entangled with real thermal structures. Existing IR restoration methods are typically designed for a single degradation type …

• High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences

  Tao Zhang, Peixian Su, Xingyu Gao, Yunhao Zou, Yu Lu, Zunjie Zhu, Bolun Zheng, Ying Fu, Chenggang Yan · 24. September 2026

  Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic range (HDR) reconstruction from single-exposure sequences highly challenging without alternating expos…

• Recurrent Dynamic Range Extension

  Sebastian Dille, Keru Fu, S. Mahdi H. Miangoleh, Ya\u{g}{\i}z Aksoy · 14. September 2026

  We present an approach to progressively extend the highlights of an image. Instead of reconstructing the full dynamic range of a complex scene directly, we learn a simpler task first: We extend the dynamic range of an input image by a single exposure value. Once this is mastered, we retrieve the ful…

• FujinSplat: Seeing Through Smoke with RAW-Domain Gaussian Splatting

  Gengjia Chang, Ziteng Cui, Shuhong Liu · 11. September 2026

  The appearance of a smoky scene is shaped by two processes that a camera records together: the participating medium alters scene radiance in a view-dependent way, and the image signal processor (ISP) then remaps the result through a nonlinear tone and color transformation. Recovering a clean 3D scen…

• UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

  Zhiwen Yang, Jiayin Li, Chengyu Liu, Hui Zhang, Bingzheng Wei, Yan Xu · 11. September 2026

  All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Existing methods typically prioritize modeling inter-task heterogeneity (e.g., distinct data distributions and degradation types). However, they largely …

• Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models

  Justine Giroux, Jack Oliver Hilliard, Yannick Hold-Geoffroy, Javier Vazquez-Corral, Jean-Fran\c{c}ois Lalonde · 11. September 2026

  Accurate modelling of illumination is central to realistic image synthesis and scene understanding. Yet, there is little exploration into whether image generative models are good at this task or whether physical plausibility remains a key challenge for them. Clearly, significant progress has been ma…

• Albedo Estimation via Latent Bridge Matching

  Carme Corbi, David Serrano-Lozano, Javier Vazquez-Corral, Maria Vanrell · 10. September 2026

  Recent advances in Intrinsic Image Decomposition (IID) have increasingly relied on generative models. However, progress remains limited by three key challenges: (a) insufficient physical consistency, (b) high computational cost at inference time, and (c) limited generalization capabilities. In this …

• DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing

  Mei Lu, Shangliang Shao, Shanliang Yao · 9. September 2026

  Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion jointly degrade structural and spectral information. Most deep learning methods rely on RGB inputs and spatial-domain feature extraction, which limits …

• P-PatchDiff: Progressive Patch Diffusion Models for Low-light Image Enhancement

  Ruoyu Guo, Haonan Zhong, Maurice Pagnucco, Yang Song · 2. September 2026

  Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detailed images. Patch diffusion models further offer a promising solution to size-agnostic image restoration while improving efficiency. However, existing …

• HELIOS: From midnight to noon, continuous outdoor urban scene relighting

  Hala Djeghim, Nathan Piasco, Luis Rold\~ao, Moussab Bennehar, Dzmitry Tsishkou, C\'eline Loscos, D\'esir\'e Sidib\'e · 2. September 2026

  Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely on synthetic data or paired multi-illumination supervision, which limits their generalization to the diverse and challenging conditions of real-world…

• 3D-USE: From Image-Level to Scene-Level Underwater Enhancement

  Jieyu Yuan, Yuanlin Zhang, Jihong Li, Chunle Guo, Huimin Lu, Chongyi Li · 31. August 2026

  Underwater 3D reconstruction faithfully reproduces the color shifts and visibility loss of captured views, while physical inversion may leave estimation errors in the recovered scene appearance. We formulate Underwater Scene-level Enhancement (USE) as learning a persistent, visibility-enhanced 3D sc…

• FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images

  Ruiyang Chen, Feiran Li, Heng Guo, Zhanyu Ma · 27. August 2026

  High-quality surface normal estimation is preferred for detailed surface shape recovery and image editing. Existing single image-based methods, though being a practical setup, often struggle to recover fine surface details and are sensitive to inherent shape-reflectance ambiguity. While photometric …

• LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results

  Zewei He, Xi Tong, Yu Chen, Xingyu Liu, Xin Li, Zepeng Wang, Jiagao Hu, Fuhao Li, Yuxuan Chen, Fei Wang, Daiguo Zhou, Minmin Yi, Chuanrui Zhang, Liwen Zhang, Yeongjin Jeong, Hyunjin Cho, Jiwon Lee, Minsang Kim, Jae Woong Soh, Jin-Hui Jiang, Rong-Lin Jian, Chih-Chung Hsu, Youngjin Oh, Junhyeong Kwon, Junyoung Park, Jae Hyun Park, Sung Ju Lee, Nam Ik Cho, Vishwajeet Shukla, Himanshu Baurai, Zhiqi Zhang, Kui Jiang, Zhaocheng Yu, Runzhe Li, Dawei Fan, Hao Li, Zhanshuo Zhang, Fan Ji, Jiangmeng Li, Xiongxin Tang, Fanjiang Xu, Shangquan Sun, Anh-Kiet Duong, Petra Gomez-Kr\"amer, Jean-Michel Carozza, Ruibo Zhang, Dexiang Hong, Xinyan Liu, Shengeng Tang, Weidong Chen, Tzu-Hsuan Weng, Min-Te Sun · 25. August 2026

  This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequently encountered practical problem in the field of autonomous driving, i.e., raindrop-reflection composite degradation on rainy days. This competitio…

• BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement

  Yi Ai, Zheng Chen, Yuanhao Cai, Yulun Zhang, Xiaokang Yang · 25. August 2026

  Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport can model exposure ambiguity; however, its flexibility may also alter observable geometry and chromatic content. Moreover, fixed invertible color coor…

• Image-Conditional Diffusion Transformer for Underwater Image Enhancement

  Xingyang Nie, Caoliang Zhang, Xiaoyu Zhai, Fengzhong Qu, Biao Wang, Huilin Ge · 25. August 2026

  Underwater image enhancement (UIE) has attracted much attention owing to its importance for underwater operation and marine engineering. Motivated by the recent advance in generative models, we propose a novel UIE method based on image-conditional diffusion transformer (ICDT). Our method takes the d…

• DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration

  Zhaokun He, Kangbiao Shi, Axi Niu, Jian Jin, Peng Wu, Wei Dong, Qingsen Yan · 21. August 2026

  All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling and lack low-level visual priors during reconstruction, leading to structural distortions and semantic inconsistencies. To…