Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate the color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and under-exposed regions display opposite color tone distribution shifts with respect to each other, which may not be easily normalized in joint modeling as they usually do not have ``normal-exposed'' regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project webpage: https://github.com/yiyulics/CSEC.
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；City University of New York / CSI（纽约城市大学 / 斯塔滕岛学院）
Color in an image is resolved into 3 or 4 color components and 2-Dimages of these components are stored in separate channels. Most of the color image enhancement algorithms are applied channel-by-channel on each image. But such a system of color image processing is not processing the original color. When a color image is represented as a quaternion image, processing is done in original colors. This paper proposes an implementation of the quaternion approach of enhancement algorithm for enhancing color images and is referred as the modified alpha-rooting by the two-dimensional quaternion discre…
Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfu…
We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produce…
In this paper, we propose a mathematical model for color image processing. It is a logarithmical one. We consider the cube (-1,1)x(-1,1)x(-1,1) as the set of values for the color space. We define two operations: addition <+> and real scalar multiplication <x>. With these operations the space of colors becomes a real vector space. Then, defining the scalar product (.|.) and the norm || . ||, we obtain a (logarithmic) Euclidean space. We show how we can use this model for color image enhancement and we present some experimental results.
This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipulation steps, many of which are nonlinear in nature, to render the initial sensor image to its final photo-finished representation saved in the 8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the major procedures applied by the ISP for color correction, this …
Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；National University of Singapore（新加坡国立大学）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the last few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that eff…
Qingdao University（青岛大学）；Ocean University of China（中国海洋大学）
Images captured underwater are often characterized by low contrast, color distortion, and noise. To address these visual degradations, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement. In the proposed framework, Difference of Gaussian (DoG) filter and bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Specially, the…
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；City University of New York（纽约城市大学）
A novel method of color image enhancement is proposed, in which three or four color channels of the image are transformed to one channel 2-D grayscale image. This paper describes different models of such transformations in the RGB and other color models. Color image enhancement is achieved by enhancing first the transformed grayscale image and, then, transforming back the grayscale image into the colors. The color image enhancement is done on the transformed 2-D grayscale image rather than on the color image. New algorithms of color image enhancement are described in both frequency and time do…
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…
Underwater images are inevitably affected by color distortion and reduced contrast. Traditional statistic-based methods such as white balance and histogram stretching attempted to adjust the imbalance of color channels and narrow distribution of intensities a priori thus with limited performance. Recently, deep-learning-based methods have achieved encouraging results. However, the involved complicate architecture and high computational costs may hinder their deployment in practical constrained platforms. Inspired by above works, we propose a statistically guided lightweight underwater image en…
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；City University of New York / CSI（纽约市立大学/斯塔滕岛学院）
In this paper a quaternion approach of enhancement method is proposed in which color in the image is considered as a single entity. This new method is referred as the alpha-rooting method of color image enhancement by the two-dimensional quaternion discrete Fourier transform (2-D QDFT) followed by a spatial transformation. The results of the proposed color image enhancement method are compared with its counterpart channel-by-channel enhancement algorithm by the 2-D DFT. The image enhancements are quantified to the enhancement measure that is based on visual perception referred as the color enh…
Pacific University, Udaipur（太平洋大学）；Sachdeva Engineering College for Girls, Mohali（萨赫德瓦女子工程学院）
The production of a printed product involves three stages: prepress, the printing process (press) itself, and finishing (post press). There are various types of equipments (printers, scanners) and various qualities image are present in the market. These give different color rendering each time during reproduction. So, a color key tool has been developed keeping Color Management Scheme (CMS) in mind so that during reproduction no color rendering takes place irrespective of use of any device and resolution level has also been improved.
City University of Hong Kong（香港城市大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；Australian National University（澳大利亚国立大学）；Beijing Jiaotong University（北京交通大学）；Nankai University（南开大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple c…
VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Advanced Technology, University of Science and Technology of China（先进科技研究院，中国科学技术大学）；CHN Energy Digital Intelligence Technology Development (Beijing) Co., LTD.（中能数字智能技术发展（北京）有限公司）
Dayananda Sagar College of Engineering（达亚南达·萨加尔工程学院）
A new approach for tuning the parameters of MultiScale Retinex (MSR) based color image enhancement algorithm using a popular optimization method, namely, Particle Swarm Optimization (PSO) is presented in this paper. The image enhancement using MSR scheme heavily depends on parameters such as Gaussian surround space constant, number of scales, gain and offset etc. Selection of these parameters, empirically and its application to MSR scheme to produce inevitable results are the major blemishes. The method presented here results in huge savings of computation time as well as improvement in the vi…
A Perceptually Inspired Variational Framework for Color Enhancement
Department of Electrical Engineering, Universidad de Chile（智利大学电气工程系）；Dipartimento di Tecnologia dell’Informazione, Università di Milano（米兰大学信息科技技术系）；Departament de Tecnologia, Universitat Pompeu Fabra（庞培法布拉大学技术系）
FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder
College of Computer Science & Technology, Qingdao University（青岛大学计算机科学与技术学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
Dayananda Sagar College of Engineering（达亚南达·萨加尔工程学院）
This paper presents a new color image enhancement technique based on modified MultiScale Retinex(MSR) algorithm and visual quality of the enhanced images are evaluated using a new metric, namely, wavelet energy. The color image enhancement is achieved by down sampling the value component of HSV color space converted image into three scales (normal, medium and fine) following the contrast stretching operation. These down sampled value components are enhanced using the MSR algorithm. The value component is reconstructed by averaging each pixels of the lower scale image with that of the upper sca…
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）
Image enhancement is an important stage in the image-processing domain. The most known image enhancement method is the histogram equalization. This method is an automated one, and realizes a simultaneous modification for brightness and contrast in the case of monochrome images and for brightness, contrast, saturation and hue in the case of color images. Simple and efficient methods can be obtained if affine transforms within logarithmic models are used. A very important thing in the affine transform determination for color images is the coordinate system that is used for color space representa…
School of Computer Science, Wuhan University（武汉大学计算机学院）；University of Central Florida（中佛罗里达大学）；Wuhan Institute of Technology（武汉工程大学）；National Tsing Hua University（清华大学（台湾））
Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness. Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation (diffuse illumination color plus environment illumination color), we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refin…
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；College of Computer and Control Engineering, Northeast Forestry University（东北林业大学计算机与控制工程学院）；College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））；Fujian Ocean Innovation Center, Xiamen（福建海洋创新中心，厦门）
Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；South China University of Technology（华南理工大学）
In recent years, single image dehazing deep models based on Atmospheric Scattering Model (ASM) have achieved remarkable results. But the dehazing outputs of those models suffer from color shift. Analyzing the ASM model shows that the atmospheric light factor (ALF) is set as a scalar which indicates ALF is constant for whole image. However, for images taken in real-world, the illumination is not uniformly distributed over whole image which brings model mismatch and possibly results in color shift of the deep models using ASM. Bearing this in mind, in this study, first, a new non-homogeneous atm…
Shanghai Jiao Tong University（上海交通大学）；Alibaba Group（阿里巴巴集团）；Dalian University of Technology（大连理工大学）
The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretized 3D lattice. Previous works have made efforts to learn image-adaptive output color values of LUTs for flexible enhancement but neglect the importance of sampling strategy. They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the…
Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years. These algorithms developed upon various assumptions demonstrate successes from various aspects using different data sets and different metrics. In this work, we setup an undersea image capturing system, and construct a large-scale Real-world Underwater Image Enhancement (RUIE) data set divided into three subsets. The three subsets target at three challenging aspects for enhancement, i.e., image visibility quality, color casts, and higher-le…
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…
We present a wavelet-based dual-stream network that addresses color cast and blurry details in underwater images. We handle these artifacts separately by decomposing an input image into multiple frequency bands using discrete wavelet transform, which generates the downsampled structure image and detail images. These sub-band images are used as input to our dual-stream network that incorporates two sub-networks: the multi-color space fusion network and the detail enhancement network. The multi-color space fusion network takes the decomposed structure image as input and estimates the color corre…
Beijing University of Posts and Telecommunications（北京邮电大学）
Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self-regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value). We build a reflectance estimation network by restricting the consistency of reflectances embedded in both the original and a novel …
Enhancement is an important step in post-processing digital images for personal use, in medical imaging, and for object recognition. Most existing manual techniques rely on region selection, similarity, and/or thresholding for editing, never really considering the topological structure of the image. In this paper, we leverage the contour tree to extract a hierarchical representation of the topology of an image. We propose 4 topology-aware transfer functions for editing features of the image using local topological properties, instead of global image properties. Finally, we evaluate our approac…
Dayananda Sagar College of Engineering（达亚南达·萨加尔工程学院）
This paper presents the development of a new algorithm for Gaussian based color image enhancement system. The algorithm has been designed into architecture suitable for FPGA/ASIC implementation. The color image enhancement is achieved by first convolving an original image with a Gaussian kernel since Gaussian distribution is a point spread function which smoothen the image. Further, logarithm-domain processing and gain/offset corrections are employed in order to enhance and translate pixels into the display range of 0 to 255. The proposed algorithm not only provides better dynamic range compre…
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 …
Images captured in low-light environment often suffer from complex degradation. Simply adjusting light would inevitably result in burst of hidden noise and color distortion. To seek results with satisfied lighting, cleanliness, and realism from degraded inputs, this paper presents a novel framework inspired by the divide-and-rule principle, greatly alleviating the degradation entanglement. Assuming that an image can be decomposed into texture (with possible noise) and color components, one can specifically execute noise removal and color correction along with light adjustment. Towards this pur…
Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function
Informatics Institute for Postgraduate Studies, Iraqi Commission for Computer and Informatics（伊拉克计算机与信息委员会研究生信息学研究所）；Al-Khwarizmi College of Engineering, University of Baghdad（巴格达大学阿尔-花剌子密工程学院）
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Computer Science, Xi’an University of Architecture and Technology（西安建筑科技大学计算机学院）
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…
LLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment
School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；University of Electronic Science and Technology of China（电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；Dalian University of Technology（大连理工大学）
