Pair comparison based progressive subjective quality ranking for underwater images

Underwater images contain an interacting mixture of distortions due to the physicochemical properties of the water, suspended organic matter and floating particles in water. Unlike images in traditional natural image quality databases, underwater images are often difficult to acquire with reference images and sets of images with gradient distortion. Therefore, it is even more difficult for the viewers to assign an absolute psychophysical scale to the quality of underwater images. In this paper, we propose a pairwise subjective comparison procedure for underwater images quality ranking inspired by the intuitive suppression and competence mechanisms in visual perception. In the proposed method, we construct a preselection based initial image quality dataset by full pairwise comparison, which also enables online adaptive new image updating. The proposed method is not constrained by the lack of reference images, and is reliable and sensitive to images with discriminable distortion level and various image contents. The proposed pairwise comparison further allows an uncertain choice, which does not require a reinforce human opinion. To the best of our knowledge, this is the first implementation for underwater image subjective quality ranking, and a new approach to the image quality ranking for different image contents with unknown distortion level. We demonstrate that the obtained subjective image ranking correlates well with the human perception of quality difference among the underwater images than that of the single stimuli image quality assessment with finite labor burden. Moreover, our proposed method accurately characterize the gradual degradation in the underwater image sequence taken in controlled conditions. The proposed progressive learning ranking is also an alternative way to realize adaptive extension of the existing image quality databases.     

融合深度学习与多尺度Retinex的水下图像增强方法

针对水下图像纹理模糊和色偏严重等问题,提出了一种融合深度学习与多尺度导向滤波Retinex的水下图像增强方法。首先,将陆上图像采用纹理和直方图匹配法进行退化,构建退化水下图像失真的数据集并训练端到端卷积神经网络(convolutional neural network,CNN) 模型,利用该模型对原始水下图像进行颜色校正,得到色彩复原后的水下图像;然后,对色彩复原图像的亮度通道,采用多尺度Retinex(multi-scale Retinex,MSR) 方法得到纹理增强图像;最后,融合色彩复原图像中的颜色分量和纹理增强图像得到最终水下增强图像。本文利用仿真水下图像数据集和真实水下图像对提出方法进行性能测试。实验结果表明,所提方法的均方根误差、峰值信噪比、CIEDE2000和水下图像质量评价指标分别为0.302 0、17.239 2 dB、16.878 4和4.960 0,优于5种对比方法,增强后的水下图像更加真实自然。本文方法在校正水下图像颜色失真的同时,能有效提升纹理清晰度和对比度。     

Color correction and restoration based on multi-scale recursive network for underwater optical image

Underwater image processing has played an important role in various fields such as submarine terrain scanning, submarine communication cable laying, underwater vehicles, underwater search and rescue. However, there are many difficulties in the process of acquiring underwater images. Specifically, the water body will selectively absorb part of the light when light travels through the water, resulting in color degradation of underwater images. At the same time, due to the influence of floating substances in the water, the light has a certain degree of scattering, which will bring serious problems such as blurred details and low contrast to underwater images. Therefore, using image processing technology to restore the real appearance of underwater images has a high practical value. In order to solve the above problems, we combine the color correction method with the deblurring network to improve the quality of underwater images in this paper. Firstly, aiming at the problem of insufficient number and diversity of underwater image samples, a network combined with depth image reconstruction and underwater image generation is proposed to simulate underwater images based on the style transfer method. Secondly, for the problem of color distortion, we propose a dynamic threshold color correction method based on image global information combined with the loss law of light propagation in water. Finally, in order to solve the problem of image blurring caused by scattering and further improve the overall image clarity, the color-corrected image is reconstructed by a multi-scale recursive convolutional neural network. Experiment results show that we can obtain images closer to underwater style with shorter training time. Compared with several latest underwater image processing methods, the proposed method has obvious advantages in multiple underwater scenes. Simultaneously, we can restore the color information, remove blurring and boost detail for underwater images.     

基于暗原色先验模型的水下彩色图像增强算法

针对在水下环境中,光的散射和衰减导致水下光学成像质量严重下降,图像对比度低、颜色失真的问题,提出了一种暗原色先验和基于通道直方图量化的颜色校正算法相结合的图像增强新方法。对于待增强的水下彩色图像,首先建立水下光学图像成像模型,并利用优化与改进的暗原色先验算法对图像进行去模糊,然后通过分析R、G、B三通道的累积直方图,对去模糊后的彩色图像各通道灰度值进行量化,实现图像的颜色校正。实验结果表明,提出的方法可以有效地消除了由于光的散射造成图像的模糊,有效提高了水下图像的视觉效果,恢复水下图像的颜色平衡。     

沙尘环境下视频图像增强方法的研究

针对沙尘暴环境下,因大气介质中悬浮的沙尘颗粒的散射作用导致的视频图像质量严重下降,清晰度与对比度降低,边缘模糊等问题,本文提出了一种空间域与变换域相结合的沙尘图像增强方法。先将降质图像转换到模糊域进行全局沙尘图像的PAL模糊增强;然后在空间域利用带限局部直方图自适应均衡算法对局部分量进行增强处理,最后利用POSHE算法对局部细节再次进行增强处理。实验结果表明,所提方法能够有效的提高沙尘图像的对比度,突出图像细节与边缘信息,增强了图像的整体视觉效果。     

一种水下图像增强算法的实现和仿真

〗由于水下环境的特殊性和复杂性,使得水下图像的质量差、图像对比度低。文中给出了一种基于空间域的图像增强方法,该算法利用均值算法估计水下图像背景,从原水下图像衰减背景图像,再对衰减背景之后的图像进行改进的图像锐化处理。通过对算法仿真结果的分析可知,处理后的图像整体对比度明显提升,同时使得目标的边缘更加清晰。     

基于非均匀圆偏振光照射的水下偏振图像处理方法

针对浑浊水下成像环境,提出了基于非均匀圆偏振光照射的浑浊水下偏振图像处理方法。首先对非均匀圆偏振光照明条件下目标成像和处理遇到的困难进行了分析,并对圆偏振光作为光源的特点进行阐述;其次设计成像环境对水下目标物的偏振图像和可见光图像进行采集。采用基于CLAHE和MSRCR的方法对可见光图像进行预处理,大幅度消除非均匀光照对图像清晰度的影响;依据水下成像的物理模型,考虑到水下目标物反射光的偏振特性,提出了一种基于偏振信息图像融合的图像去雾方法,实验证明该方法能够对偏振信息图像进行优势融合并增强其边缘轮廓;考虑到人眼视觉特性,将偏振信息融合图像与优化后的可见光图像进行融合,最终实现对水下模糊偏振图像的复原。结果表明,该方法实现了偏振图像边缘轮廓及纹理的增强,显著提高了水下图像的质量。     

Depth-aware total variation regularization for underwater image dehazing

Underwater images often show severe quality degradation due to the light absorption and scattering effects in water medium. This paper introduces a scene depth regularized underwater image dehazing method to obtain high-quality underwater images. Unlike previous underwater image dehazing methods that usually calculate a transmission map or a scene depth map using priors, we construct an exponential relationship between transmission map and normalized scene depth map. An initial scene depth is first estimated by the difference between color channels. Then it is refined by total variation regularization to keep structures while smoothing excessive details. An alternating direction algorithm is given to solve the optimization problem. Extensive experiments demonstrate that the proposed method can effectively improve the visual quality of degraded underwater images, and yields high-quality results comparative to the state-of-the-art underwater image enhancement methods quantitatively and qualitatively.     

雾天偏振成像影响分析及复原方法研究

为了抑制雾天图像质量的退化,基于大气散射物理模型及偏振图像暗通道原理,提出了一种改进的雾天偏振遥感图像去雾算法。首先依据大气散射模型对雾天偏振成像机理进行分析,对大气偏振信息对去雾的影响进行了阐述。其次利用边缘检测和闭运算自动获取雾天偏振图像的天空区域,估算无穷远大气光强和大气偏振度。最后,针对图像中存在的噪声干扰等因素,修正大气偏振度及大气光强,恢复了退化图像的辐射强度信息。通过理论分析和实验验证,取得了较好的雾天图像复原结果。结果表明,该算法可以准确获取天空区域,实现更高鲁棒性的天空区域估计方法,有效提高图像的对比度和清晰度,增加图像细节,改善雾天图像的质量。该算法能够有效抑制雾天对图像造成的退化,从而提高遥感的目标探测和识别能力。     

Underwater image enhancement with global–local networks and compressed-histogram equalization

Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global–local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.     

Low bit-rate compression of underwater image based on human visual system

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.     

A novel dark channel prior guided variational framework for underwater image restoration

Image captured underwater often suffers from low contrast, color distortion and noise problems, which is caused by absorbing and scattering before the light reaches the camera when traveling through water. Underwater image enhancement and restoration from a single image is known to be an ill-posed problem. To overcome these limitations, we establish an underwater total variation (UTV) model relying on underwater dark channel prior (UDCP), in which UDCP is used to estimate the transmission map. We design the data item and smooth item of the unified variational model based on the underwater image formation model. We further employ the alternating direction method of multipliers (ADMM) to accelerate the solving procedure. Numerical experiential results demonstrate that our underwater variational method obtains a good outcome on dehazing and denoising. Furthermore, compared with several other state-of-the-art algorithms, the proposed approach achieves better visual quality, which is illustrated by examples and statistics.     

一种SAR与可见光图像压缩感知融合增强方法

针对机载多传感器成像战场态势感知的问题，提出了一种合成孔径雷达(Synthetic Aperture Radar，SAR)与可见光图像压缩感知融合增强方法。该方法首先对SAR与可见光图像分别进行压缩感知测量，得到压缩测量值，然后通过基于局部权值的融合方法实现对压缩测量值的融合，再利用有序度最优分割法提取SAR图像的强散射目标，最后对融合测量值重建得到初步融合图像，初步融合图像通过目标对比度增强得到最终融合图像。对多组图像进行了仿真分析，视觉及数值结果表明该方法能显著增强融合图像的目标对比度，提升了图像纹理清晰度，较大程度降低了图像融合过程中的数据计算量。     

Underwater image enhancement based on conditional generative adversarial network

Underwater images play an essential role in acquiring and understanding underwater information. High-quality underwater images can guarantee the reliability of underwater intelligent systems. Unfortunately, underwater images are characterized by low contrast, color casts, blurring, low light, and uneven illumination, which severely affects the perception and processing of underwater information. To improve the quality of acquired underwater images, numerous methods have been proposed, particularly with the emergence of deep learning technologies. However, the performance of underwater image enhancement methods is still unsatisfactory due to lacking sufficient training data and effective network structures. In this paper, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear underwater image is achieved by a multi-scale generator. Besides, we employ a dual discriminator to grab local and global semantic information, which enforces the generated results by the multi-scale generator realistic and natural. Experiments on real-world and synthetic underwater images demonstrate that the proposed method performs favorable against the state-of-the-art underwater image enhancement methods.     

Underwater image enhancement by wavelength compensation and dehazing

Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.     

S3: a spectral and spatial measure of local perceived sharpness in natural images

This paper presents an algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then, the adjusted measures are combined via a weighted geometric mean. The resulting measure, i.e., S(3) (spectral and spatial sharpness), yields a perceived sharpness map in which greater values denote perceptually sharper regions. This map can be collapsed into a single index, which quantifies the overall perceived sharpness of the whole image. We demonstrate the utility of the S(3) measure for within-image and across-image sharpness prediction, no-reference image quality assessment of blurred images, and monotonic estimation of the standard deviation of the impulse response used in Gaussian blurring. We further evaluate the accuracy of S(3) in local sharpness estimation by comparing S(3) maps to sharpness maps generated by human subjects. We show that S(3) can generate sharpness maps, which are highly correlated with the human-subject maps.     

基于水下光照不均匀成像模型的图像清晰化算法

为了克服水下图像清晰度低和光照不均问题,首先在水下光照均匀成像模型的基础上建立水下光照不均匀条件下的成像模型,进而提出新的水下图像清晰化算法。在算法中,首先在小波变换低频子带上实现了介质散射光和光照变化混合图像的快速估计与去除,然后将得到的图像分割成亮斑区和散射区,并分别进行增强处理。实验结果表明,本文提出的算法可以显...     

DewaterNet: A fusion adversarial real underwater image enhancement network

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different datasets and metrics. In this paper, we utilize an effective and public underwater benchmark dataset including diverse underwater degradation scenes to enlarge the test scale and propose a fusion adversarial network for enhancing real underwater images. Meanwhile, the multiple inputs and well-designed multi-term adversarial loss can not only introduce multiple input image features, but also balance the impact of multi-term loss functions. The proposed network tested on the benchmark dataset achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, the ablation study experimentally validates the contributions of each component and hyper-parameter setting of loss functions.     

改进的伽马校正与多尺度融合的水下图像增强

由于光在水下传播会发生吸收和散射,导致采集 的水下图像出现模糊、对比度低、色偏、光照不 均匀等问题。针对以上问题,提出了一种改进的伽马校正与多尺度融合的水下图像增强算法 。首先基于G 通道对R和B通道进行补偿,并对RGB 三通道进行直方图拉伸后使用灰度世界(Gray World) 算法得到颜 色校正图像;然后使用改进的伽马函数改善颜色校正后图像光照不均匀问题,得到光照均匀 图像,并进 行归一化处理;再对光照均匀图像使用限制对比度的自适应直方图均衡化(contrast limite d adaptive histogram equalization,CLAHE)算法得到对比度提升图像;最后采用多尺度融 合算法对以上得出的3幅图 片进行融合,得出增强图像。实验结果表明,提出的算法对不同水下环境的图像均有较好的处理 效果,图像质量评价指标得到明显提高。