基于颜色校正和去模糊的水下图像增强方法

由于光在水下传播时会出现吸收和散射的情况,水下图像往往存在色偏、对比度低、模糊、光照不均匀等问题。根据水下图像成像模型,人们在海底拍摄所获得的图像往往是退化的图像,而退化的图像不能完整地表达海洋场景信息,难以满足实际的应用需要。为此,文中提出了一种基于颜色校正和去模糊的水下图像增强方法。该方法有效融合了颜色校正和去模糊两个阶段,取得了递增的增强效果。在颜色校正阶段,首先对原始图像进行对比度拉伸,在对比度拉伸完成之后,图像可能存在拉伸过度或拉伸不足的现象。因此,所提方法根据灰度世界先验,在对比度拉伸后进一步使用伽马校正来优化和调整图像的对比度和色彩,使图像的R,G,B三通道的灰度值之和趋于相等。接着,在去模糊阶段,通过融合暗通道先验对颜色校正后的图像进行去模糊,得到最终的增强图像。实验结果表明,所提方法具有良好的整体恢复效果,能有效地恢复图像信息,在主观评价和客观评价上均展现出较好的效果。另外,所提方法可以作为水下图像分类等计算机视觉任务的预处理步骤,在实验中能够将水下图像集的分类精度提升16%左右。     

Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

The demand for the exploration of ocean resources is increasing exponentially. Underwater image data plays a significant role in many research areas. Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation. Therefore, visual enhancement has become an essential process to recover the required data from the images. Many algorithms had been proposed in a decade for improving the quality of images. This paper aims to propose a single image enhancement technique without the use of any external datasets. For that, the degraded images are subjected to two main processes namely, color correction and image fusion. Initially, veiling light and transmission light is estimated to find the color required for correction. Veiling light refers to unwanted light, whereas transmission light refers to the required light for color correction. These estimated outputs are applied in the scene recovery equation. The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques. The resultants are divided into three weight maps namely, luminance, saliency, chromaticity and fused using the Laplacian pyramid. The results obtained are graphically compared with their input data using RGB Histogram plot. Finally, image quality is measured and tabulated using underwater image quality measures.     

水下光学图像重建方法研究进展

水下光学图像可以提供直观丰富的海洋信息,近年来在海洋资源开发、环境保护和海洋工程等诸多领域发挥越来越重要的作用。但是受恶劣复杂的水下成像环境影响,水下光学图像普遍存在对比度低、图像模糊以及颜色失真等质量退化问题,严重制约水下智能处理系统的性能和应用。如何清晰地重建水下光学图像是国内外广泛关注的、具有挑战性的难点问题。随着深度学习技术的蓬勃发展,利用深度学习来提升水下图像质量成为当前的研究热点。鉴于目前国内在水下光学图像重建方面的研究综述较少,本文全面综述其研究进展。分析了水下图像退化机理,总结了现有水下成像模型以及水下图像重建的挑战;梳理了水下光学图像重建方法的发展历程,根据是否采用深度学习以及是否基于成像模型,将现有方法分为4大类,并按照研究发展顺序,依次介绍4类方法的基本思想,分析其优缺点;归纳了目前公开的水下图像数据集以及常用的水下图像质量评价方法,并对8种典型的水下图像重建方法进行了性能评测和对比分析;总结了该领域目前仍存在的问题,展望了后续研究方向,以便于相关研究人员了解该领域的研究现状,促进该领域的技术发展。     

Visual enhancement of underwater images using Empirical Mode Decomposition

Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing.     

The Retinex based improved underwater image enhancement

The underwater images suffer from low contrast and color distortion due to variable attenuation of light and nonuniform absorption of red, green and blue components. In this paper, we propose a Retinex-based underwater image enhancement approach. First, we perform underwater image enhancement using the contrast limited adaptive histogram equalization (CLAHE), which limits the noise and enhances the contrast of the dark components of the underwater image at the cost of blurring the visual information. Then, in order to restore the distorted colors, we perform the Retinex-based enhancement of the CLAHE processed image. Next, in order to restore the distorted edges and achieve smoothing of the blurred parts of image, we perform bilateral filtering on the Retinex processed image. In order to utilize the individual strengths of CLAHE, Retinex and bilateral filtering algorithms in a single framework, we determine the suitable parameter values. The qualitative and quantitative performance comparison with some of the existing approaches shows that the proposed approach achieves better enhancement of the underwater images.

     

基于暗原色先验原理的水下图像增强

针对水下光衰减和散射导致的图像严重降质问题和用传统方法进行水下图像增强 产生色偏现象,提出一种新的水下图像增强方法。基于暗原色先验原理进行水下图像增强,用 软抠图的方法对图像暗通道进行细化;在图像前0.1%最亮的像素点中,用中值滤波算法计算出 这些像素点的中值,再计算这些像素点和与之对应的中值的差值,差值最小的像素点作为背景 光的预估值,并用该像素点所在区域颜色饱和度方差来判断预估背景光的准确性;利用Retinex 算法和图像各颜色通道的衰减系数比对增强后的图像进行颜色校正。实验表明,该方法能有效 地去除水下图像中的雾色、校正图像色偏问题,进而提高图像对比度。     

结合导向滤波与自适应算子的水下图像增强

针对水下图像存在的颜色失真、对比度低及图像模糊等现象,提出一种结合导向滤波与自适应算子的水下增强算法。首先根据水体对光线吸收的差异,对水下图像的三通道进行自适应算子颜色补偿,融合三通道,得到颜色补偿后的水下图像,有效提升水下图像色彩真实性;再将水下图像放入导向滤波改进的Retinex模型中,有效去除水下图像产生的模糊现象;最后分别计算增强后水下图像的三种权重,根据三种权重进行多尺度融合,获得最终增强后的水下图像。选取不同的水下图像进行客观评价实验与主观评价实验,再与现阶段的水下图像增强算法进行对比,实验结果说明该算法在修正水下图像颜色及增强水下图像对比度等方面具有较好的效果,符合人眼视觉特征,视觉效果优于现有的水下图像增强算法。     

不同颜色模型下自适应直方图拉伸的水下图像增强

目的 水下图像是海洋信息的重要载体,然而与自然环境下的图像相比,其成像原理更复杂、对比度低、可视性差。为保证不同类型水下图像的增强效果,本文提出在两种颜色模型下自适应直方图拉伸的水下图像增强方法。方法 首先,进行基于Gray-World理论对蓝、绿色通道进行颜色均衡化预处理。然后,根据红绿蓝（R-G-B）通道的分布特性和不同颜色光线在水下传播时的选择性衰减,提出基于参数动态优化的R-G-B颜色模型自适应直方图拉伸,并采用引导滤波器降噪。接下来,在CIE-Lab颜色模型,对‘L’亮度和‘a’‘b’色彩分量分别进行线性和曲线自适应直方图拉伸优化。最终,增强的水下图像呈现出高对比度、均衡的饱和度和亮度。结果 选取不同类型的水下图像作为数据集,将本文方法与融合颜色模型（ICM）、非监督颜色纠正模型（UCM）、基于暗通道先验性（DCP）的水下图像复原和基于水下暗通道先验（UDCP）的图像复原方法相比较,增强后的图像具有高对比度和饱和度。定性和定量分析实验结果说明本文提出的方法能够获得更好视觉效果,增强后的图像拥有更高信息熵和较低噪声。结论 在RGB颜色模型中,通过合理地考虑水下图像的分布特性和水下图像退化物理模型提出自适应直方图拉伸方法;在CIE-Lab颜色模型中,引入拉伸函数和指数型曲线函数重分布色彩和亮度两个分量,本方法计算复杂度低,适用于不同复杂环境下的水下图像增强。     

多残差联合学习的水下图像增强

目的 由于海水中悬浮的颗粒会吸收和散射光,并且不同波长的光在海水中的衰减程度也不同,使得水下机器人拍摄的图像呈现出对比度低、颜色失真等问题。为解决上述问题以呈现出自然清晰的水下图像,本文提出了基于神经网络的多残差联合学习的方法来对水下图像进行增强。方法 该方法包括3个模块:预处理、特征提取和特征融合。首先,采用Sigmoid校正方法对原始图像的对比度进行预处理,增强失真图像的对比度,得到校正后的图像;然后,采用双分支网络对特征进行提取,将原始图像送入分支1——残差通道注意分支网络,将校正后的图像与原始图像级联送入分支2——残差卷积增强分支网络。其中,通道注意分支在残差密集块中嵌入了通道注意力模型,通过对不同通道的特征重新进行加权分配,以加强有用特征;卷积增强分支通过密集级联和残差学习,提取校正图像中边缘等高频信息以保持原始结构与边缘。最后,在特征融合部分,将以上双分支网络的特征级联后,通过残差学习进一步增强;增强后的特征与分支1的输出、分支1与分支2的输出分别经过自适应掩膜进行再次融合。选取通用UIEB(underwater image benchmark dataset)数据集中的8...     

融合深度学习与成像模型的水下图像增强算法

水下机器人的视觉感知功能因受到水下环境因素的影响,面临着图像质量降低的挑战,如图像颜色畸变、整体色调偏绿、偏蓝、对比度较低、细节较为模糊等。提出一种结合深度学习方法与物理成像模型的新型水下图像增强算法,通过构建包含扩张卷积和带参数激活函数的神经网络,进行背景散射光和直接传输映射的估计,并结合成像模型的数学表达进行重建运算得到增强后图像。实验结果表明,与UDCP、IBLA、GLNet等典型图像增强算法相比,该算法具有更快的运算速度,且能够消除水下环境因素带来的影响,丰富图像色彩的同时能增强各类细节,在峰值信噪比指标和结构相似度指标上取得了较大值。此外,增强后的图像在特征点匹配实验中获得了更好的匹配效果。     

Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm

Underwater imagery suffers from severe effects due to selective attenuation and scattering effects when light travels through water medium. Such damages limit the ability of vision tasks and reduce image quality. There are a lot of enhancement methods to improve the quality of underwater image. However, most of the methods produce distortion effects in the output images. The proposed natural-based underwater image color enhancement (NUCE) method consists of four steps. The first step introduces a new approach to neutralize underwater color cast. The inferior color channels are enhanced based on gain factors, which are calculated by considering the differences between the superior and inferior color channels. In the second step, the dual-intensity images fusion based on average of mean and median values is proposed to produce lower-stretched and upper-stretched histograms. The composition between these histograms improves the image contrast significantly. Next, the swarm-intelligence based mean equalization is proposed to improve the naturalness of the output image. Through the fusion of swarm intelligence algorithm, the mean values of inferior color channels are adjusted to be closed to the mean value of superior color channel. Lastly, the unsharp masking technique is applied to sharpen the overall image. Experiments on underwater images that are captured under various conditions indicate that the proposed NUCE method produces better output image quality, while significantly overcoming other state-of-the-art methods.     

基于人工欠曝光融合和白平衡技术的水下图像增强算法

获得清晰准确的水下图像是人类探索水下世界的重要前置条件。然而与平常图像相比,水下图像往往具有对比度低、细节保留不足及颜色失真等问题,这导致其视觉效果不佳。针对上述问题,提出了基于人工欠曝光融合和白平衡技术（AUF+WB）的水下图像增强算法。首先,利用调节伽马值的方式对原始水下图像进行操作,从而生成5幅相应的欠曝光图像;然后,以对比度、饱和度及良好曝光度作为融合权重,并结合多尺度融合来生成融合图像;最后,将各类颜色通道补偿后的图像分别结合灰色世界假设白平衡生成相应的白平衡图像,再利用水下彩色图像质量评价指标（UCIQE）及水下图像质量评价标准（UIQM）对得到的白平衡图像进行评价。通过选取不同类型的水下图像作为实验样本,将AUF+WB算法与现存先进的水下图像去雾算法进行比较,结果表明AUF+WB算法在图像质量定性、定量两方面分析中和对比算法相比均有更好的表现。所提出的AUF+WB算法可矫正水下图像的颜色失真,并增强其对比度、恢复其细节,有效提升了水下图像的视觉质量。     

一种基于色彩补偿的水下图像综合增强算法

针对退化的水下图像在高级视觉分析任务中无法进行有效的目标检测及识别的问题,提出了一种通过色彩补偿和对比度拉伸,HSV空间γ校正和亮度通道去模糊系列方法实现了对水下图像的色彩校正、色彩对比度、饱和度和细节清晰度的综合提高.其中,提出了基于高斯滤波的亮度通道去散射方法,并对典型水体水下图像综合增强参数进行了分析.实验对比了...     

面向真实水下图像增强的质量评价数据集

目的 由于光在水中的衰减/散射以及微生物对光的吸收/反射等影响,水下图像通常存在色偏、模糊、光照不均匀以及对比度过低等诸多质量问题。研究人员对此提出了许多不同的水下图像增强算法。为了探究目前已有的水下图像增强算法的性能和图像质量客观评价方法是否适用于评估水下图像,本文开展大规模主观实验来对比不同水下图像增强算法在真实水下图像数据集上的性能,并对现有图像质量评价方法用于评估水下图像的准确性进行测试。方法 构建了一个真实的水下图像数据集,其中包含100幅原始水下图像以及对应的1 000幅由10种主流水下图像增强方法增强后的图像。基于成对比较的策略开展水下图像主观质量评价,进一步对主观评价得到的结果进行分析,包括一致性分析、收敛性分析以及显著性检验。最后将10种现有主流的无参考图像质量评价在本文数据集上进行测试,检验其在真实水下图像数据集上的评价性能。结果 一致性分析中,该数据集包含的主观评分有较高的肯德尔一致性系数,其值为0.41;收敛性分析中,所收集的投票数量与图像数量足够得到稳定的主观评分;表明本文构建的数据集具有良好的有效性与可靠性。此外,目前对比自然图像的无参考图像质量评价方法并不...     

结合细节信息的自适应Retinex算法水下图像增强

针对Retinex算法应用于水下图像增强中,常出现颜色失真与图像细节增强相矛盾的现象,提出了结合细节信息的自适应多尺度Retinex水下图像增强算法。分析包含不同细节信息的水下图像对Retinex算法增强中卷积函数尺度大小的选择要求;采用图像梯度作为调节因子,自适应调整多尺度Retinex算子的权重,用于适应包含不同细节信息的水下图像对对比度增强的要求,有效地缓和了水下图像增强在颜色失真和细节对比度提升之间的矛盾。多组实验验证了该算法在去除水下图像的蓝绿背景、避免颜色失真、消除非均匀光照和图像细节增强等方面均优于传统多尺度和颜色保真的多尺度Retinex算法。     

结合背景光融合及水下暗通道先验和色彩平衡的#br# 水下图像增强

水下环境、光线衰减和拍摄方式造成水下图像具有不同色调、对比度和模糊度.基于图像成像模型的水下图像复原方法通常基于暗通道先验或最大像素先验,容易受到水下复杂环境的干扰而输出低质量的复原图像,因此文中提出基于背景光融合及水下暗通道先验和色彩平衡的水下图像增强方法.首先,提出多候选背景光融合方法,估计正确的背景光.然后,基于高质量水下图像统计得出水下暗通道先验,计算更准确的RGB分量传输地图.将复原图像从RGB颜色模型转换到CIE-Lab颜色模型,对L亮度分量和a、b色彩分量分别进行归一化拉伸和优化调整,进一步提高复原后水下图像的亮度和对比度.多种定性和定量分析说明文中方法增强的图像在对比度、亮度和颜色上的显示效果优于大部分现有的水下图像增强方法复原的图像.     

采用双尺度图像分解的水下彩色图像增强

目的 为解决水下图像的色偏和低对比度等问题,提出一种基于双尺度图像分解的水下彩色图像增强算法。方法 通过基于均值和方差的对比度拉伸方法改善图像的色偏问题,并利用中值滤波降低红通道对比度拉伸后引入的噪声;采用双尺度图像分解绿通道图像补偿红通道图像细节;在处理后的红通道图像中引入原始图像红通道的真实细节与颜色。结果 选取不同水下图像作为实验数据集,将本文方法与暗通道先验的方法、基于融合的方法、自动红通道恢复方法以及一种基于卷积神经网络深度学习的方法相比较,首先从主观视觉效果进行定性分析,然后通过不同评测指标进行定量分析。主观定性分析结果表明,提出的方法相比较其他方法能够更好地解决图像色偏和红色阴影问题;定量分析中,自然图像质量评价（natural image quality evaluation,NIQE）指标和信息熵（information entropy,IE）值较基于融合的方法和深度学习的方法分别提高了1.8%和13.6%,且水下图像质量评价指标（underwater image quality measurement method,UIQM）较其他方法更优。结论 提出的双尺度图像分解方法利用水下图像成像特点解决图像色偏以及低对比度问题,具有良好的适应能力,同时算法复杂度低且鲁棒性较高,普遍适用于复杂的水下彩色图像增强。     

Underwater image restoration based on exponentiated mean local variance and extrinsic prior

Due to the absorption and scattering of light when it travels in water, underwater imaging has various problems, such as color distortion and low contrast. In general, it is difficult to accurately estimate the transmission map of underwater image during the restoration process, while it is easy to introduce external noise. In view of the above two problems, firstly, we estimate the original transmission map of underwater images by the intrinsic boundary constraint based on the scene radiance. Then, we develop a novel variational framework combined with the exponentiated mean local variance and extrinsic prior of transmission map for keeping the image edge and removing noise. Finally, we make quantitative and qualitative analyses of the restored underwater images. The experiments demonstrate that the method proposed in this paper has certain advantages compared with other methods. In quantitative comparison, our proposed method has higher image quality evaluation score. For qualitative analysis, the images restored using our method not only have natural colors and good contrast, but the details of the images are also well maintained.

     

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

This paper presents a real‐time and channel‐invariant visibility enhancement algorithm using a hybrid image enhancement approach. The proposed method is initially motivated by an underwater visual simultaneous localization and mapping (SLAM) failure in a turbid medium. The environments studied contain various particles and are dominated by a different image degradation model. Targeting image enhancement for degraded images but not being limited to it, the proposed method provides a highly effective solution for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method introduces a hybrid scheme of two image enhancement modules: a model‐based (extensive) enhancement and a model‐free (immediate) enhancement. The proposed method is validated by using simulated synthetic color images and real‐world color and grayscale underwater images. Real‐world validation is performed in various environments such as hazy indoor, smoky indoor, and underwater. Using the ground truth trajectory or clear images acquired from the same area but without turbidity, we evaluate the proposed visibility enhancement and camera registration improvement for a feature based (ORB‐SLAM2), a direct (LSD‐SLAM), and a visual underwater SLAM application.     

Adaptive enhancement of underwater images using multi-objective PSO

Underwater images have poor clarity and bad contrast due to low illumination in deep water. Moreover, underwater images are bluish-green in appearance due to inherent wavelength absorption property of water. Therefore, the study of underwater images is a difficult task. Being computationally simple, histogram-based enhancement techniques are obvious choice for improvement of contrast and color of underwater images. However, due to lack of any guidance mechanism, these techniques can overstretch the histogram leading to artifacts in the image. Hence, an adaptive method named ‘Contrast and Information Enhancement of Underwater Images’ (CIEUI) is proposed, which enhances underwater images by improving their contrast and information content using Multi-Objective Particle Swarm Optimization (MOPSO). Objective functions of MOPSO are chosen to act as guiding mechanism to ensure color & contrast correction and information enhancement respectively without introducing artifacts. Computed results not only have good contrast and color performance but also have better information content. The proposed CIEUI technique performs quantitatively and qualitatively better as compared to state-of-the-art algorithms.