Spike camera is a new type of bio-inspired vision sensor that records light intensity in the form of a spike array with high temporal resolution (20,000 Hz). This new paradigm of vision sensor offers significant advantages for many vision tasks such as high speed image reconstruction. However, existing spike-based approaches typically assume that the scenes are with sufficient light intensity, which is usually unavailable in many real-world scenarios such as rainy days or dusk scenes. To unlock more spike-based application scenarios, we propose a Recurrent Spike-based Image Restoration (RSIR) network, which is the first work towards restoring clear images from spike arrays under general illumination. Specifically, to accurately describe the noise distribution under different illuminations, we build a physical-based spike noise model according to the sampling process of the spike camera. Based on the noise model, we design our RSIR network which consists of an adaptive spike transformation module, a recurrent temporal feature fusion module, and a frequency-based spike denoising module. Our RSIR can process the spike array in a recursive manner to ensure that the spike temporal information is well utilized. In the training process, we generate the simulated spike data based on our noise model to train our network. Extensive experiments on real-world datasets with different illuminations demonstrate the effectiveness of the proposed network. The code and dataset are released at https://github.com/BIT-Vision/RSIR.
All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems). The loss of image details due to A/D quantization is complete and it cannot be recovered by traditional image processing methods, but the modern data-driven machine learning approach offers a much needed cure to the problem. In this work we propose a novel approach to restore and enhance images acquired in low and uneven lighting. First, the ill illumination is algorithmically compensated by emulating the effects …
SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration
School of Big Data and Software Engineering, Chongqing University（大数据与软件工程学院，重庆大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Microelectronics and Communication Engineering, Chongqing University（微电子与通信工程学院，重庆大学）；School of Electrical and Information Engineering, Tianjin University（电气与信息工程学院，天津大学）
Rain removal plays an important role in the restoration of degraded images. Recently, data-driven methods have achieved remarkable success. However, these approaches neglect that the appearance of rain is often accompanied by low light conditions, which will further degrade the image quality. Therefore, it is very indispensable to jointly remove the rain and enhance the light for real-world rain image restoration. In this paper, we aim to address this problem from two aspects. First, we proposed a novel entangled network, namely EMNet, which can remove the rain and enhance illumination in one …
Robust Low-light Scene Restoration via Illumination Transition
The Hong Kong University of Science and Technology（香港科学与技术大学）；The Hong Kong Polytechnic University（香港理工大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；University of Surrey（萨里大学）
VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook
South China University of Technology（华南理工大学）；Pazhou Laboratory（琶洲实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Fujian Normal University（福建师范大学）
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）；City University of Hong Kong（香港城市大学）；Centre for Artificial Intelligence and Robotics, HKISI-CAS（中国科学院香港智能科学与健康研究院人工智能与机器人中心）；University College London（伦敦大学学院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；The University of Sydney（悉尼大学）
针对胶囊内窥镜图像不同曝光水平校正难题，提出可提示扩散 Transformer 框架 EndoUIC 及 CEC 数据集，在多数据集上优于 SOTA 并具临床应用价值。
University of Electronic Science and Technology of China（电子科技大学）
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color bias, unknown noise, detail loss and halo artifacts. In this paper, we propose a very fast deep learning framework called Bringing the Lightness (denoted as BLNet) that consists of two U-Nets with a series of well-designed loss functions to tackle all of the above degradations. Based on Retinex Theory, the decomposition net in our model can decompose low-light images into reflectance and illumination and remove noise in the reflectance duri…
SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement
Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（海洋科学与空间信息学院，中国石油大学（华东））；School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast（电子、电气与计算机科学学院，女王大学 Belfast）；Department of Automation, Tsinghua University（自动化系，清华大学）；College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）
Bahcesehir University（巴赫切谢希尔大学）；American University of Malta（马耳他美国大学）
Clinical screening with low-quality fundus images is challenging and significantly leads to misdiagnosis. This paper addresses the issue of improving the retinal image quality and vessel segmentation through retinal image restoration. More specifically, a cycle-consistent generative adversarial network (CycleGAN) with a convolution block attention module (CBAM) is used for retinal image restoration. A modified UNet is used for retinal vessel segmentation for the restored retinal images (CBAM-UNet). The proposed model consists of two generators and two discriminators. Generators translate image…
This contribution deals with image restoration in optical systems with coherent illumination, which is an important topic in astronomy, coherent microscopy and radar imaging. Such optical systems suffer from wavefront distortions, which are caused by imperfect imaging components and conditions. Known image restoration algorithms work well for incoherent imaging, they fail in case of coherent images. In this paper a novel wavefront correction algorithm is presented, which allows image restoration under coherent conditions. In most coherent imaging systems, especially in astronomy, the wavefront…
Xi’an University of Science and Technology（西安科技大学）
A novel color image enhancement method is proposed based on Retinex to enhance color images under non-uniform illumination or poor visibility conditions. Different from the conventional Retinex algorithms, the Weighted Guided Image Filter is used as a surround function instead of the Gaussian filter to estimate the background illumination, which can overcome the drawbacks of local blur and halo artifact that may appear by Gaussian filter. To avoid color distortion, the image is converted to the HSI color model, and only the intensity channel is enhanced. Then a linear color restoration algorit…
Dual High-Order Total Variation Model for Underwater Image Restoration
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, the School of Automation and Electrical Engineering, University of Science and Technology Beijing（北京科技大学自动化与电气工程学院 工业过程知识自动化教育部重点实验室）
GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
1 Graduate School of Engineering, Tohoku University, Sendai, Japan 2 Dept. of Radiology \& Biomedical Imaging, Yale University, New Haven, CT, USA 3 Dept. of Computer Science；Information Engineering, National Taiwan University, Taipei, Taiwan 4 Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan 5 Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
Progressive Image Restoration via Text-Conditioned Video Generation
Department of Computer Science, University of Illinois Springfield（伊利诺伊大学斯普林菲尔德分校计算机科学系）；School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）
Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (HSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of HSI restoration methods have been developed, yet their effectiveness in enhancing low-light HSIs is constrained. This work focuses on the low-light HSI enhancement task, which aims to reveal the spatial-spectral information hidden in darkened areas. To facilitate the development of low-light HSI processing, we collect a low-light HSI (LHSI) dataset of both indoor and ou…
Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学系，柏林技术大学）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（学习与数据基础研究院（BIFOLD））
Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework
Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；School of Automation, China University of Geosciences（中国地质大学自动化学院）；Department of Electromechanical Engineering, University of Macau（澳门大学机电工程系）
Gaussian Light Field Splatting: A Physical Prior-Driven Vision Transformer for Unsupervised Low-Light Image Enhancement
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Herbert Wertheim College of Engineering, University of Florida（佛罗里达大学赫伯特·韦特海姆工程学院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；National Innovation Center of Intelligent and Connected Vehicles（国家智能网联汽车创新中心）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in a low signal-to-noise ratio. Most of the previous works on low-light image processing focus either only on a single task such as illumination adjustment, color enhancement, or noise removal; or on a joint illumination adjustment and denoising task that heavily relies on short-long exposure image pairs collected from specific camera models, and thus these approaches are less practical and generalizable in real-world settings where camera-spec…
Civil Aviation Flight University of China（中国民用航空飞行学院）；University of Electronic Science and Technology of China（电子科技大学）；Institute of North Electronic Equipment（北方电子设备研究所）；Pensees Ptd Ltd（澎思科技有限公司）；Baidu Research（百度研究院）；Tencent AI Lab（腾讯人工智能实验室）；National University of Singapore（新加坡国立大学）
In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given l…
School of Aeronautics and Astronautics, Sichuan University（四川大学航空航天学院）；University of Electronic Science and Technology of China（电子科技大学）；College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）
CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation
Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University（厦门大学水声通信与海洋信息技术教育部重点实验室）；PAMI Research Group, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系PAMI研究组）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
Fusion-Restoration Image Processing Algorithm to Improve the High-Temperature Deformation Measurement
College of Aerospace Science and Engineering（航空航天科学与工程学院）；National University of Defense Technology（国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）
Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Sustainability X-Lab, The University of Hong Kong（香港大学可持续发展X实验室）；School of Architecture, Building, and Civil Engineering, Loughborough University（拉夫堡大学建筑、建造与土木工程学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；NEC Laboratories America（美国NEC实验室）；Tianyi Traffic Technology（天易交通科技）；Duke University（杜克大学）
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on. The low illumination, light reflections, scattering, absorption and suspended particles inevitably lead to the critically degraded underwater image quality, which causes great challenges on recognizing the objects from the underwater images. The existing underwater enhancement methods that aim to promote the underwater visibility, heavily suffer from the poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we intr…
LightQANet: Quantized and Adaptive Feature Learning for Low-Light Image Enhancement
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机视觉研究院）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学人工智能与先进计算学院）；School of Mathematics and Statistics, Changsha University of Science and Technology（长沙理工大学数学与统计学院）；School of Computer Science, Sichuan Normal University（四川师范大学计算机科学学院）；Department of Computer Science, University of Nottingham Ningbo China（宁波大学计算机科学系）
