More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training data.To address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms.This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images.In the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images.In the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image enhancement.Experiments show the effectiveness and rationality of each module designed in this paper.And the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.

Toward Robust and Efficient Low-Light Image Enhancement:Progressive Attentive Retinex Architecture Search

In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

Poor illumination greatly affects the quality of obtained images.In this paper,a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the frequency and content information of images and is divided into three subnetworks:decomposition,enhancement,and adjustment networks,which perform image decomposition;denoising,contrast enhancement,and detail preservation;and image adjustment and generation,respectively.The model is trained on the public LOL dataset,and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.

Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform

Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the valuable information from such an image cannot be fully extracted for further processing.To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers,pre-processing of a sea cucumber image is attracting increasing interest.This paper presents a newmethod based on contrast limited adaptive histogram equalization and wavelet transform(CLAHE-WT)to enhance the sea cucumber image quality.CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution,and WTwas used for de-noising based on a soft threshold.Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details.For quantitative analysis,the test with 120 underwater images showed that for the proposed method,the mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were 49.2098,13.3909,and 6.6815,respectively.The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.

MAGAN:Unsupervised Low-Light Image Enhancement Guided by Mixed-Attention

Most learning-based low-light image enhancement methods typically suffer from two problems.First,they require a large amount of paired data for training,which are difficult to acquire in most cases.Second,in the process of enhancement,image noise is difficult to be removed and may even be amplified.In other words,performing denoising and illumination enhancement at the same time is difficult.As an alternative to supervised learning strategies that use a large amount of paired data,as presented in previous work,this paper presents an mixed-attention guided generative adversarial network called MAGAN for low-light image enhancement in a fully unsupervised fashion.We introduce a mixed-attention module layer,which can model the relationship between each pixel and feature of the image.In this way,our network can enhance a low-light image and remove its noise simultaneously.In addition,we conduct extensive experiments on paired and no-reference datasets to show the superiority of our method in enhancing low-light images.

Retinex based low-light image enhancement using guided filtering and variational framework

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.

Low-light color image enhancement based on NSST

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this paper.The steps of the proposed algorithm are described as follows.First,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value component.Next,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency sub-bands.Then,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST transform.Finally,the image is converted back to the RGB color space to obtain the enhanced image.Experimental results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.

深海载人潜水器多约束先验图像增强方法

针对深海载人潜水器人造光源照射造成深海图像颜色失真、散射模糊以及光照不均匀等图像降质问题,提出一种基于多约束先验的贝叶斯Retinex深海载人潜水器图像增强方法。该方法首先通过一种基于统计的颜色校正方法对图像进行色彩校正处理,然后对光照图先后进行平滑、结构和不均匀光照高亮区域3种先验,并将3种先验条件结合到贝叶斯模型当中,并选取Lab颜色空间中L分量作为初始光照图来进行光照图估计的最优化求解。最后,通过伽马校正的方法对光照图和反射图进行处理,获得增强后的深海图像。实验结果表明,所提出的方法平均运行时间4.39 s,具有较低的复杂度,更加适合深海恶劣环境下的图像增强,处理得到的深海图像具有更好的观测效果。

基于改进暗通道先验的海上低照度图像增强算法

针对基于暗通道先验的低照度图像增强算法在处理极端海上低光环境下图像时会存在光晕效应、色彩失真的问题,提出了1种基于暗通道先验的自适应海上低照度图像增强算法。首先,通过选取图像类型划分指标,将数据集中的图像分类,并通过Otsu方法和图像直方图分布,获取图像的区域划分阈值,将图像进行划分得到局部区域图,分析各类图像的局部区域图之间的关系;最后,通过对不同的局部区域图采用不同的改进暗通道先验算法进行处理,将1个图像中的2个增强后局部区域图合并,得到整张图像的增强结果,并对增强后图像进行主客观的图像质量评价。实验结果表明,该算法解决了现有算法在处理极端海上低照度图像时存在光晕效应和色彩失真的问题,并使不同环境下的海上低照度图像都能达到较好的恢复效果。

多波束测深和图像分割的海底地貌边界提取研究

为了获取完整清晰海底地貌边界,为海洋工程建设与环境监测等提供科学依据,设计多波束测深和图像分割的海底地貌边界提取方法。采用多波束测深系统采集海底地貌测深数据,利用数据生成海底地貌深度图像,经灰度化、滤波及增强处理此深度图像后,获取高质量海底地貌灰度图像,结合全局阈值分割方法分割此灰度图像,获得图像内海底地貌边界部分,实现海底地貌边界提取。结果表明,该方法根据测深数据生成的图像质量好,对比度高,暗处细节清晰,提取的海底地貌边界清晰、完整,具有较高的实际应用价值。

基于Sea-thru和Mask R-CNN的深海多金属结核图像处理

利用现代图像处理技术探测矿区内矿物基本形态结构、分布状态以及多金属结核的其他赋存数据时,为了进一步提高深海矿物评估精度,提出了基于Sea-thru和Mask R-CNN相结合的深海矿物图像分割系统。实验结果表明,Sea-thru能够对海底矿物图像进行有效增强,还原衰退的矿物图像,Mask R-CNN可提高矿物颗粒分割准确率,进而获取更加准确的矿物颗粒信息,提升矿物资源的评估精度。

A Dual-Tree Complex Wavelet Transform-Based Model for Low-Illumination Image Enhancement

Image enhancement is a monumental task in the field of computer vision and image processing.Existing methods are insufficient for preserving naturalness and minimizing noise in images.This article discusses a technique that is based on wavelets for optimizing images taken in low-light.First,the V channel is created by mapping an image’s RGB channel to the HSV color space.Second,the acquired V channel is decomposed using the dual-tree complex wavelet transform(DT-CWT)in order to recover the concentrated information within its high and low-frequency subbands.Thirdly,an adaptive illumination boost technique is used to enhance the visibility of a low-frequency component.Simultaneously,anisotropic diffusion is used to mitigate the high-frequency component’s noise impact.To improve the results,the image is reconstructed using an inverse DT-CWT and then converted to RGB space using the newly calculated V.Additionally,images are white-balanced to remove color casts.Experiments demonstrate that the proposed approach significantly improves outcomes and outperforms previously reported methods in general.

海面复杂背景下图像增强算法研究

该文提出一种改进的基于去雾理论的夜间低照度图像增强算法。通过对暗通道先验去雾算法在处理夜间复杂灯光图像中存在的伪光晕、亮度不准等问题进行分析,采用一种可以边缘保持的滤波方法进行暗通道求取,并针对图像特点对大气光值进行精确估计,结合采样方法提升处理效率,实现对低照度图像的有效增强。经过实验分析,该算法能有效地防止光晕现象,改善图像的亮度和噪声。

基于对比度受限直方图均衡化的水下海参图像增强方法

针对水下图像受到水下复杂光照的影响导致图像对比度差的现象,采用对比度受限自适应直方图均衡化方法(contrast-limited adaptive histogram equalization,CLAHE)对水下海参图像进行增强处理,算法首先将原始图像分割成若干个子区域并且大小相同,再选取特定值对每个子区域的直方图进行截取,并将截取下的像素均匀分配到每个灰度级,最终得到限定对比度直方图。并通过研究算法中的相关参数,得到适用于水下海参图像增强的参数值,取得了更好的增强效果。通过评价函数均方差(mean squared error,MSE),峰值信噪比(peak signal to noise rate,PSNR)和信息熵(information entropy)对比CLAHE方法和其他一些方法,结果显示CLAHE算法在水下海参图像提高质量和保持图像细节方面表现出更好的性能,为以后水下机器人的识别定位提供了方便。

海天背景下红外舰船自动目标识别算法

海天背景下红外舰船目标的自动识别是武器跟踪导引的重要内容。针对低信噪比和低对比度的红外图像,首先采取均值平滑滤波和指数增强对红外图像进行预处理,然后在So-bel算子检测梯度的基础上,采用最小二乘迭代拟合出海天线。通过区域生长与合并分割图像,提取出表征舰船目标的典型形状与灰度特征,并进行加权融合以实现对潜在目标的自动识别。实验结果表明该算法能以较高准确率实时识别出舰船目标。

海域图像增强方法综述

由于在海域条件下,受到雾气、光线等多种气象因素影响,光电装备获取的图像出现不同程度的退化现象,导致图像的显示质量下降。为了提高图像增强方法在海域环境下应用的有效性,本文针对海域图像海雾及照度不均的特点,分析了4种常用的图像增强算法,包括基于直方图均衡化的图像增强算法、基于暗原色原理的去雾算法、基于Retinex的图像增强算法、基于同态滤波的图像增强算法,并总结了这些算法的特点及其使用场景。上述4种图像增强算法应用于海域环境下图像增强,选择合适的增强算法可以有效提升图像对比度,克服海域图像海雾及照度不均问题。

海面红外图像的动态范围压缩及细节增强

动态范围压缩和细节增强是红外图像处理的两个重要课题。为了将高动态海面背景红外图像清晰显示,提出一种高动态范围压缩及细节增强算法。首先,通过基于梯度边缘信息的多方向拉普拉斯增强方法,将梯度图像平滑处理,并与多方向拉普拉斯滤波相乘,实现高动态范围图像的细节增强;然后统计增强后图像的动态广义直方图信息;最后采用灰度级分组的方法构造映射函数,将高动态范围压缩到8 bits,输出可清晰显示的红外图像。对大量海面背景红外图像进行实验分析,结果表明,该算法提高了图像的对比度,有效增强了舰船目标细节,同时抑制了海面背景噪声的放大和光晕现象的产生,最终获得较好的输出图像。

提高水雷探测效果的声纳图像增强算法

对灭雷具系统中显控台对高分辨率图像声纳信号进行转换的计算机辅助探测算法进行了探索,重点介绍了一种能够增强声纳图像中目标信号显示效果的先进算法。从而可以使声纳对目标扫描后的图像更加清晰,与背景对比效果更加明显。为便于理解和比较,同时还引用了已经被国外普遍应用的三种其它算法。

VEViD:Vision Enhancement via Virtual diffraction and coherent Detection

The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In modern times,this idea has been extended,for example,to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves.Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks.Specifically,diffraction followed by coherent detection becomes an image enhancement tool.Vision Enhancement via Virtual diffraction and coherent Detection(VEViD)reimagines a digital image as a spatially varying metaphoric“lightfield”and then subjects the field to the physical processes akin to diffraction and coherent detection.The term“Virtual”captures the deviation from the physical world.The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction.Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image.The phase of the output,not the intensity,represents the output image.VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm.The algorithm is extremely fast,interpretable,and reduces to a compact and intuitively-appealing mathematical expression.We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks.The application of VEViD to color enhancement is also demonstrated.