This paper proposes a self-supervised low light image enhancement method based on deep learning. Inspired by information entropy theory and Retinex model, we proposed a maximum entropy based Retinex model. With this model, a very simple network can separate the illumination and reflectance, and the network can be trained with low light images only. We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning. Our model is very simple and does not rely on any well-designed data set (even one low light image can complete the training). The network only needs minute-level training to achieve image enhancement. It can be proved through experiments that the proposed method has reached the state-of-the-art in terms of processing speed and effect.
A self-supervised adaptive low-light video enhancement method, called SALVE, is proposed in this work. SALVE first enhances a few key frames of an input low-light video using a retinex-based low-light image enhancement technique. For each keyframe, it learns a mapping from low-light image patches to enhanced ones via ridge regression. These mappings are then used to enhance the remaining frames in the low-light video. The combination of traditional retinex-based image enhancement and learning-based ridge regression leads to a robust, adaptive and computationally inexpensive solution to enhance…
When capturing images in low-light conditions, the images often suffer from low visibility, which not only degrades the visual aesthetics of images, but also significantly degenerates the performance of many computer vision algorithms. In this paper, we propose a self-supervised low-light image enhancement framework (SID-NISM), which consists of two components, a Self-supervised Image Decomposition Network (SID-Net) and a Nonlinear Illumination Saturation Mapping function (NISM). As a self-supervised network, SID-Net could decompose the given low-light image into its reflectance, illumination …
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning
Southern University of Science and Technology（南方科技大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；New York University（纽约大学）；Beijing Information Science and Technology University（北京信息科技大学）
A Two-Stage Multi-Scale Attention-Based Network for Weakly Supervised Cataract Fundus Image Enhancement
Hunan Institute of Technology（湖南工学院）；National and Local Joint Engineering Laboratory of Computer Aided Design（计算机辅助设计国家地方联合工程实验室）；Dalian University of Technology（大连理工大学）
Low light conditions in aerial images adversely affect the performance of several vision based applications. There is a need for methods that can efficiently remove the low light attributes and assist in the performance of key vision tasks. In this work, we propose a new method that is capable of enhancing the low light image in a self-supervised fashion, and sequentially apply detection and segmentation tasks in an end-to-end manner. The proposed method occupies a very small overhead in terms of memory and computational power over the original algorithm and delivers superior results. Addition…
Images obtained under low-light conditions will seriously affect the quality of the images. Solving the problem of poor low-light image quality can effectively improve the visual quality of images and better improve the usability of computer vision. In addition, it has very important applications in many fields. This paper proposes a DEANet based on Retinex for low-light image enhancement. It combines the frequency information and content information of the image into three sub-networks: decomposition network, enhancement network and adjustment network. These three sub-networks are respectivel…
Xi’an Jiaotong University（西安交通大学）；Stony Brook University（石溪大学）；University of Texas Southwestern Medical Centre（得克萨斯大学西南医学中心）
Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting energy properties of the scanned object. Due to the limited photon numbers and the non-ideal detector response of each energy channel, the reconstructed images usually contain much noise. With the development of Deep Learning (DL) technique, different kinds of DL-based models have been proposed for noise reduction. However, most of the models require clean data set as the training labels, which are not always available in medical imaging field. Inspiring by the similarities of…
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
Department of Computer Science, South China Normal University（华南师范大学计算机科学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of …
University of Rochester（罗切斯特大学）；ByteDance Inc.（字节跳动公司）；Tencent Inc.（腾讯公司）
In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion. Conventional unsupervised learning-based approaches usually tackle the low-light image enhancement problem using an image-to-image translation model. They focus primarily on illumination or contrast enhancement but fail to suppress the noise that ubiquitously exists in images taken under real-world low-light conditions. To address this issue, we explicitly decouple this task into two sub-tasks: illumination enhancement and noise suppression. We propose to learn a two…
Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately. However, such data-driven models ignore the inherent characteristics of transformation between the low and normal light images, leading to unstable training and artifacts. Here, we propose to leverage the invertible network to enhance low-light image in forward process and degrade the normal-light one inversely with unpaired learning. The generated and real images are then fed into discriminators for adversaria…
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
Northwestern Polytechnical University（西北工业大学）；Science and Technology on Electromechanical Dynamic Control Laboratory（机电动态控制重点实验室）；Key Laboratory of Intelligent Interaction and Applications (Northwestern Polytechnical University), Ministry of Industry and Information Technology（工业和信息化部智能交互与应用重点实验室（西北工业大学））
During the past years,deep convolutional neural networks have achieved impressive success in low-light Image Enhancement.Existing deep learning methods mostly enhance the ability of feature extraction by stacking network structures and deepening the depth of the network.which causes more runtime cost on single image.In order to reduce inference time while fully extracting local features and global features.Inspired by SGN,we propose a Attention based Broadly self-guided network (ABSGN) for real world low-light image Enhancement.such a broadly strategy is able to handle the noise at different e…
Tongji University（同济大学）；Nanyang Technological University（南洋理工大学）；Meituan（美团）；City University of Hong Kong（香港城市大学）
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images. However, for unsupervised low light enhancement, the remaining noise suppression issue due to the lacking of supervision of detailed signal largely impedes the wide deployment of these methods in real-world applications. Herein, we propose a novel Cycle-Interactive Generative Adversarial Network (CIGAN) for unsupervised low-light image enhancement, which is capable of not only better transferring illuminatio…
The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to a sub-optimal performance. In this paper, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on imp…
A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a low-complexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets t…
Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, eq…
University of Science and Technology of China（中国科学技术大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Low-light images captured in the real world are inevitably corrupted by sensor noise. Such noise is spatially variant and highly dependent on the underlying pixel intensity, deviating from the oversimplified assumptions in conventional denoising. Existing light enhancement methods either overlook the important impact of real-world noise during enhancement, or treat noise removal as a separate pre- or post-processing step. We present \underline{C}oordinated \underline{E}nhancement for \underline{R}eal-world \underline{L}ow-light Noisy Images (CERL), that seamlessly integrates light enhancement …
Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark. Simply adjusting the brightness of a low-light image will inevitably amplify those artifacts. To address this difficult problem, this paper proposes a novel end-to-end attention-guided method based on multi-branch convolutional neural network. To this end, we first construct a synthetic dataset with carefully designed low-light simulation strategies. The dataset is much larger and more diverse than existing…
Shanghai University（上海大学）；University of Leicester（莱斯特大学）
Low light very likely leads to the degradation of an image's quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancemen…
In this paper, we present a novel low-light image enhancement method called dark region-aware low-light image enhancement (DALE), where dark regions are accurately recognized by the proposed visual attention module and their brightness are intensively enhanced. Our method can estimate the visual attention in an efficient manner using super-pixels without any complicated process. Thus, the method can preserve the color, tone, and brightness of original images and prevents normally illuminated areas of the images from being saturated and distorted. Experimental results show that our method accur…
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…
University of Science and Technology of China（中国科学技术大学）；Shandong University（山东大学）；The University of Sydney（悉尼大学）
Contrast enhancement and noise removal are coupled problems for low-light image enhancement. The existing Retinex based methods do not take the coupling relation into consideration, resulting in under or over-smoothing of the enhanced images. To address this issue, this paper presents a novel progressive Retinex framework, in which illumination and noise of low-light image are perceived in a mutually reinforced manner, leading to noise reduction low-light enhancement results. Specifically, two fully pointwise convolutional neural networks are devised to model the statistical regularities of am…
Night photography typically suffers from both low light and blurring issues due to the dim environment and the common use of long exposure. While existing light enhancement and deblurring methods could deal with each problem individually, a cascade of such methods cannot work harmoniously to cope well with joint degradation of visibility and textures. Training an end-to-end network is also infeasible as no paired data is available to characterize the coexistence of low light and blurs. We address the problem by introducing a novel data synthesis pipeline that models realistic low-light blurrin…
South China University of Technology（华南理工大学）；Peng Cheng Laboratory（鹏城实验室）；Tencent AI Lab（腾讯AI实验室）；Stony Brook University（石溪大学）；National University of Singapore（新加坡国立大学）
Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low-light image enhancement is about improving the visibility of low-light images. As the measurement noise in low-light images is usually significant yet complex with spatially-varying characteristic, how to handle the noise effectively is an important yet challenging problem in low-light image enhancement. Based on the Retinex decomposition of natural images, this paper proposes a deep learning method for low-light im…
A Frequency-Aware Self-Supervised Learning for Ultra-Wide-Field Image Enhancement
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China（浙江工业大学信息工程学院）；Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China（中国科学院宁波材料技术与工程研究所）；Department of Eye and Vision Science, University of Liverpool, Liverpool, UK（利物浦大学眼科学与视觉科学系）；School of Biomedical Engineering, ShanghaiTech University, Shanghai, China（上海科技大学生物医学工程学院）
CUHK-Sensetime Joint Laboratory, The Chinese University of Hong Kong（香港中文大学-商汤联合实验室，香港中文大学）
We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with …
LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image Enhancement
School of Computer and Information, Hefei University of Technology（合肥工业大学计算机与信息学院）；Department of Automation, University of Science and Technology of China（中国科学技术大学自动化系）
A Semi-supervised Physics-Aware Triple-Stream Underwater Image Enhancement Network
State Key Laboratory of Physical Oceanography and the Faculty of Information Science and Engineering, Ocean University of China（物理海洋学国家重点实验室和中国海洋大学信息科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen university（中山大学深圳校区计算机科学与技术学院）；Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology（深圳视觉目标检测与识别重点实验室，哈尔滨工业大学）
Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling
Department of Computer Engineering, College of Computing and Mathematics, King Fahd University of Petroleum & Minerals (KFUPM)（计算机工程系，计算与数学学院，国王法赫德石油与矿物大学）；SDAIA–KFUPM Joint Research Center for Artificial Intelligence (JRC-AI), King Fahd University of Petroleum & Minerals（人工智能联合研究中心（JRC-AI），国王法赫德石油与矿物大学）；Department of Computer Science and Information Technology, Sir Syed University of Engineering and Technology (SSUET)（计算机科学与信息科技系，西德大学工程与技术学院）；Digital Technology & Innovation Center, Siemens Healthineers, Princeton, NJ 08540, USA（数字技术与创新中心，西门子健康部，美国新泽西州普林斯顿）；Department of Aerospace Engineering, King Fahd University of Petroleum & Minerals (KFUPM)（航空航天工程系，国王法赫德石油与矿物大学）；Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals (KFUPM)（智能交通与物流跨学科研究中心，国王法赫德石油与矿物大学）
Low-light images challenge both human perceptions and computer vision algorithms. It is crucial to make algorithms robust to enlighten low-light images for computational photography and computer vision applications such as real-time detection and segmentation. This paper proposes a semantic-guided zero-shot low-light enhancement network (SGZ) which is trained in the absence of paired images, unpaired datasets, and segmentation annotation. Firstly, we design an enhancement factor extraction network using depthwise separable convolution for an efficient estimate of the pixel-wise light deficienc…
We present a novel dehazing and low-light enhancement method based on an illumination map that is accurately estimated by a convolutional neural network (CNN). In this paper, the illumination map is used as a component for three different tasks, namely, atmospheric light estimation, transmission map estimation, and low-light enhancement. To train CNNs for dehazing and low-light enhancement simultaneously based on the retinex theory, we synthesize numerous low-light and hazy images from normal hazy images from the FADE data set. In addition, we further improve the network using detail enhanceme…
The extremes of lighting (e.g. too much or too little light) usually cause many troubles for machine and human vision. Many recent works have mainly focused on under-exposure cases where images are often captured in low-light conditions (e.g. nighttime) and achieved promising results for enhancing the quality of images. However, they are inferior to handling images under over-exposure. To mitigate this limitation, we propose a novel unsupervised enhancement framework which is robust against various lighting conditions while does not require any well-exposed images to serve as the ground-truths…
University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Macau（澳门大学）；Shanghai AI Laboratory（上海人工智能实验室）；Huawei Cloud BU（华为云业务部）
Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhancement, most LLVE methods utilize temporal information from adjacent frames to restore the color and remove the noise of the target frame. However, these algorithms, based on the framework of multi-frame alignment and enhancement, may produce multi-frame fusion artifacts when encountering extreme low light or fast motion. In this paper, inspired by the low latency and high dynamic range of events, we use synthetic events…
Beijing University of Posts and Telecommunications（北京邮电大学）
It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. However, most existing methods ignore subjectivity of the task, and simply produce one result with fixed brightness. This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference. Inspired by style transfer, our method decomposes an image into two low-coupling feature components in the latent s…
AIST（产业技术综合研究所）；Tokyo Institute of Technology（东京工业大学）；NEC Corporation（日本电气股份有限公司）
A low-light image enhancement is a highly demanded image processing technique, especially for consumer digital cameras and cameras on mobile phones. In this paper, a gradient-based low-light image enhancement algorithm is proposed. The key is to enhance the gradients of dark region, because the gradients are more sensitive for human visual system than absolute values. In addition, we involve the intensity-range constraints for the image integration. By using the intensity-range constraints, we can integrate the output image with enhanced gradients preserving the given gradient information whil…
IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition
School of Mathematics and Statistics, Xidian University（西安电子科技大学数学与统计学学院）；School of Mathematics and Statistics, Xi'an Jiaotong University（西安交通大学数学与统计学学院）
Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. Inspired by human visual system, we design a multi-exposure fusion framework for low-light image enhancement. Based on the framework, we propose a dual-exposure fusion algorithm to provide an accurate contrast and lightness enhancement. Specifically, we first design the weight matrix for image fusion using illumination es…
Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex-inspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic…
Retinex model is an effective tool for low-light image enhancement. It assumes that observed images can be decomposed into the reflectance and illumination. Most existing Retinex-based methods have carefully designed hand-crafted constraints and parameters for this highly ill-posed decomposition, which may be limited by model capacity when applied in various scenes. In this paper, we collect a LOw-Light dataset (LOL) containing low/normal-light image pairs and propose a deep Retinex-Net learned on this dataset, including a Decom-Net for decomposition and an Enhance-Net for illumination adjustm…
National Chung Hsing University（国立中兴大学）；National Taichung University of Science and Technology（国立台中科技大学）
Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an ill-posed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the featu…
NTT Software Innovation Center（NTT软件创新中心）；NTT Media Intelligence Laboratories（NTT媒体智能实验室）
The rotation prediction (Rotation) is a simple pretext-task for self-supervised learning (SSL), where models learn useful representations for target vision tasks by solving pretext-tasks. Although Rotation captures information of object shapes, it hardly captures information of textures. To tackle this problem, we introduce a novel pretext-task called image enhanced rotation prediction (IE-Rot) for SSL. IE-Rot simultaneously solves Rotation and another pretext-task based on image enhancement (e.g., sharpening and solarizing) while maintaining simplicity. Through the simultaneous prediction of …
University of Science and Technology of China（中国科学技术大学）
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic. Motivated by this, several personalized enhancement methods have been investigated. However, the enhancement process based on user preferences in these techniques is invisible, i.e., a "black box". In this work, we propose an intelligible unsupervised personalized enhancer (iUPEnhancer) for low-light images, which establishes the correlations between the low-light and the unpaired reference images with regard to three user-friendly attributions (brightness, chromaticity, and noise). Th…
