Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality.     

视觉检测中的光照强度优化研究

为了实现自适应控制光照强度,保障视觉检测系统获得高质量原始图像,研究了彩色图像成像感知模型,建立了图像灰度值与光源光照强度的关系,提出基于HIS彩色空间,单独使用I分量进行光照强度评价,给出了基于数字图像的清晰度和高频信息的光照强度评价函数,并设计了光照强度调控实验系统,通过实验分析了不同测度函数对光照强度变化的评价结果,取得了与人类主观评价一致的结果。实验结果表明,通过提出的方法控制光源强度,可以使视觉系统获得高质量的原始图像,保证检测的质量和精度。     

Color Characterization and Balancing by a Nonlinear Line Attractor Network for Image Enhancement

A novel method to map high dynamic range scenes to low dynamic range images utilizing the concept of color characterization, enhancement, and balancing is described in this letter. Each pixel of the image is first characterized by extracting the relationship of the red, green, and blue components along with its corresponding neighbors using a nonlinear line attractor network to form an associative memory. Then, the illumination enhancement process is performed using a hyperbolic tangent function to provide dynamic range compression to each pixel in the image. The slope of the hyperbolic tangent function is controlled using a parameter that is determined by the local and global statistics of the image to facilitate the change of the intensity level. A color balancing process restores the original color characteristics of the image based on learned associative memory matrices which eliminate image distortion due to improper recombination of red, green and blue components after enhancement. Experiments conducted on images captured at extremely uneven lighting environments show that the proposed method outperforms other image enhancement algorithms.     

Illumination Morphing: Generating a Smooth Change of Illumination between Two Color Images

The paper proposes a method for generating a sequence of images with smooth change of illumination from two input images with different lighting conditions. The idea of the proposed method is based on image morphing. While conventional image morphing changes object shapes between two input images, here we focus on changing the illumination between two images. The proposed method uses isoluminance curves as a feature primitive. Isoluminance curves acquired from images are warped based on the correspondence of the curves between two images, and transformed luminance distributions are generated from the warped isoluminance curves. The proposed method called "illumination morphing" is able to generate smooth transition of luminance between two color images. The method does not need even the information about the light sources and 3D object models. The proposed method is a promising technique for many applications requiring a scene with variety of lighting effects, such as movies, TV games, and so on.     

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。     

场景化立体空间色彩饱和度动态修正仿真研究

当前立体空间色彩饱和度修正方法,受到噪声干扰导致图像空间的光照信息计算失准,造成低动态范围图像空间色彩饱和度失衡,从而需要二次修正,存在修正耗时较长、成本较高、且图像细节信息模糊的问题。据此提出场景化立体空间色彩饱和度动态修正方法,采用Retinex图像增强算法加入全局自适应亮度调节以及去噪环节,对图像亮度分量进行增强处理,在求解反射分量的过程中,保留场景化立体空间中的光照信息,以完成图像的预处理。将经过预处理的彩色图像压缩到普通显示器能够显示的范围内,采用阶跃方程对低动态范围内场景化立体空间色彩饱和度进行动态调整,实现场景化立体空间色彩饱和度动态修正。仿真结果表明,所提方法修正耗时较短、成本较低,并且能够更好的保存图像的细节信息。     

基于颜色恒常性的低照度图像视见度增强

在彩色成像过程中,低照度是导致图像降质的一个重要因素. 本文提出了一种新的基于颜色恒常性的低照度图像视见度增强算法. 为了避免场景光源的影响,提出了像素有效集的概念. 基于灰色调算法的灰度像素假设,利用有效像素估计光 照的颜色;在后处理阶段,利用有效像素的灰度级范围确定直方图剪裁的上下限. 实验表明,算法有效地校正了图像 的颜色、对比度和亮度,从而增强了图像的视见度,且不会产生Retinex 算法所固有的灰化效应和Halo 效应.     

Color Constancy Using the Inter-Reflection from a Reference Nose

This paper introduces a novel camera attachment for measuring the illumination color spatially in the scene. The illumination color is then used to transform color appearance in the image into that under white light.The main idea is that the scene inter-reflection through a reference camera-attached surface Nose can, under some conditions, represent the illumination color directly. The illumination measurement principle relies on the satisfaction of the gray world assumption in a local scene area or the appearance of highlights, from dielectric surfaces. Scene inter-reflections are strongly blurred due to optical dispersion on the nose surface and defocusing of the nose surface image. Blurring smoothes the intense highlights and it thus becomes possible to measure the nose inter-reflection under conditions in which intensity variation in the main image would exceed the sensor dynamic range.We designed a nose surface to reflect a blurred scene version into a small image section, which is interpreted as a spatial illumination image. The nose image is then mapped to the main image for adjusting every pixel color. Experimental results showed that the nose inter-reflection color is a good measure of illumination color when the model assumptions are satisfied. The nose method performance, operating on real images, is presented and compared with the Retinex and the scene-inserted white patch methods.     

一种新型颜色传感器设计

本文介绍一种采用脉冲光源照明、双光纤束导光的新型颜色传感器，光电探测器由光电二极管和覆盖其上方的滤色片组成。用光电积分法进行测量，在整个可见光谱区间内，对反射光谱功率进行积分，得到被测物体的颜色三刺激值X、Y、Z。同步控制脉冲光源发光与电路测量，由双光路捡测系统测量颜色三刺激值X、Y、Z和脉冲光源的微小变化，并根据后者对测试结果实时修正。该颜色传感器适合于本身不发光物体的表面颜色测量。     

基于Lambert-Phong模型的图像盲取证方法

由于现有的图像盲取证方法中所使用的光照模型不能有效地表征物体表面的实际光照效果,提出Lambert-Phong光照模型。该模型同时考虑光照的漫反射和镜面反射,利用该光照模型对无限光源模式下的图像进行蓄意修改检测。实验结果表明,Lambert-Phong光照模型能较准确地计算出图像中不同目标的光照方向,有效地判别出图像是否经过蓄意修改。     

基于照度与场景纹理注意力图的低光图像增强

目的 现有的低照度图像增强算法常存在局部区域欠增强、过增强及色彩偏差等情况,且对于极低照度图像增强,伴随着噪声放大及细节信息丢失等问题。对此,提出了一种基于照度与场景纹理注意力图的低光图像增强算法。方法 首先,为了降低色彩偏差对注意力图估计模块的影响,对低光照图像进行了色彩均衡处理;其次,试图利用低照度图像最小通道约束图对正常曝光图像的照度和纹理进行注意力图估计,为后续增强模块提供信息引导;然后,设计全局与局部相结合的增强模块,用获取的照度和场景纹理注意力估计图引导图像亮度提升和噪声抑制,并将得到的全局增强结果划分成图像块进行局部优化,提升增强性能,有效避免了局部欠增强和过增强的问题。结果 将本文算法与2种传统方法和4种深度学习算法比较,主观视觉和客观指标均表明本文增强结果在亮度、对比度以及噪声抑制等方面取得了优异的性能。在VV（Vasileios Vonikakis）数据集上,本文方法的BTMQI（blind tone-mapped quality index）和NIQMC（no-reference image quality metric for contrast distortion）指标均达到最优值;在178幅普通低照度图像上本文算法的BTMQI和NIQMC均取得次优值,但纹理突出和噪声抑制优势显著。结论 大量定性及定量的实验结果表明,本文方法能有效提升图像亮度和对比度,且在突出暗区纹理时,能有效抑制噪声。本文方法用于极低照度图像时,在色彩还原、细节纹理恢复和噪声抑制方面均具有明显优势。代码已共享在Github上：https://github.com/shuanglidu/LLIE_CEIST.git。     

基于颜色衰减先验和白平衡的水下图像复原

由于水体中悬浮颗粒对光线的吸收及散射作用,以及不同波长的光在水下的衰减程度不同,水下图像通常存在细节模糊、对比度低、颜色失真等问题。为改善水下图像质量,提出一种基于颜色衰减先验和白平衡的水下图像复原方法。首先,根据图像在HSV颜色空间中的颜色衰减先验获得场景深度图;其次,利用水下光学衰减特性估计RGB三通道对应的背景光强度与水下透射率,以实现水下图像的清晰化;最后,采用改进的白平衡方法对清晰化后的水下图像进行颜色校正。实验结果表明,该方法可以显著提升水下图像的细节清晰度与颜色保真度,视觉效果更接近自然场景下的图像。     

Linear color segmentation and its implementation

A framework for color image segmentation is presented, which combines color histogram analysis and region merging approach. Its main goal is to segment an image at material boundaries (i.e., discontinuities of reflectance properties) while ignoring spatial color inhomogeneities of uniformly pigmented (colored) objects, caused by accidents of illumination and viewing geometry. Theoretical examination of light spectrum transformations upon light reflection from material surfaces and upon interaction with a sensor system shows that in a wide variety of viewed scenes (even containing interreflections and highlight areas) uniformly pigmented objects are projected to the color space of the sensor as planar, linear, or point-like clusters, depending on lighting and viewing conditions and object geometry. To detect such clusters in the color space, three methods are suggested: Generalized Hough Transform method, gradient descent method, and eigenvectors method. A framework algorithm of color segmentation based on region merging approach is developed, which can use any of these methods. Testing this algorithm with both artificially generated and real images shows quite reliable results.     

Using eigencolor normalization for illumination-invariant color object recognition

Color is one of salient features for color object recognition, however, the colors of object images sensitively depend on scene illumination. To overcome the lighting dependency problem, a color constancy or color normalization method has to be used. This paper presents a color image normalization method, called eigencolor normalization, which consists of two phases as follows. First, the compacting method, which was originally used for compensating the adverse effect due to shape distortion for 2-D planar objects, is exploited for 3-D color space to make the color distribution less correlated and more compact. Second, the compact color image is further normalized by rotating the histogram to align with the reference axis computed. Consequently, the object colors are transformed into a new color space, called eigencolor space, which reflects the inherent colors of the object and is more invariant to illumination changes. Experimental results show that our eigencolor normalization method is superior to other existing color constancy or color normalization schemes on achieving more accurate color object recognition.     

任意光照下人脸图像的低维光照空间表示

本文提出一种不同光照条件下人脸图像的低维光照空间表示方法.这种低维光照空间表示不仅能够由输入图像估计其光照参数，而且能够由给定的光照条件生成虚拟的人脸图像.利用主成分分析和最近邻聚类方法得到9个基本点光源的位置，这9个基本点光源可以近似人脸识别应用中几乎所有的光照条件.在这9个基本光源照射下的9幅人脸基图像构成了低维人脸光照空间，它可以表示不同光照条件下的人脸图像，结合光照比图像方法，可以生成不同光照下的虚拟人脸图像.本文提出的低维光照空间的最大优点是利用某个人脸的图像建立的光照空间，可以用于不同的人脸.图像重构和不同光照下的人脸识别实验说明了本文算法的有效性.     

高质量快速全景场景再现技术

目的 色彩纠正和图像融合是生成高质量全景场景图像的关键技术。色彩纠正中参考图像的选择以及图像融合算法,决定着所生成全景图像的质量和速度。现有方法在确定一幅图像是否适合作为参考图像时,需要遍历所有其他图像,计算其作为参考图像进行色彩纠正的效果,复杂度高,速度慢;在图像融合时存在融合质量与融合速度之间的矛盾。因此,如何快速生成高质量的全景图像就成为全景场景再现的主要诉求。为此本文提出优化的参考图像自动选择的色彩纠正方法和基于重叠区域划分的分区融合方法。方法 针对参考图像选择算法复杂度高的问题,根据图像质量与稳定性通常呈反比关系的事实,采用贪婪策略,选择质量最差的图像在色彩纠正前后的相似度,作为是否选择当前图像作为参考图像的评价指标,在保证参考图像满足色彩纠正需求的前提下,大幅降低计算复杂度。针对融合质量与融合速度之间的矛盾,提出分区融合：将重叠区域划分为接缝区域和非接缝区域,利用泊松融合的接缝不可见性和线性融合实现速度快的特性分别对接缝区域和非接缝区域进行融合,既保证融合的质量,又加快融合速度。在此基础上,加入简单点光源,解决上述过程产生的光线一致性问题,进一步提高图像质量。结果 采用主观和客观相结合的方法对结果进行评估。主观方面,本文算法生成的全景图像色彩基本实现平滑过渡且图像原始信息保留完整。客观方面,色彩纠正前后图像的结构相似度（SSIM）控制在0.850.99之间,时间复杂度由原来的O（n²）降低到O（n）;分区融合后图像信息熵接近于泊松融合,但时间消耗降低72%。采用基于PC端的问卷调查法和OG-IQA算法将本文算法与PTGui、OpenCV、Xiong方法生成的全景图质量进行对比,在大多数情况下本文算法均优于上述算法。结论 实验表明,本文算法适用于多种场景,在保证目视效果良好的前提下,时间消耗降低,可广泛应用于医学、数字旅游、遥感等领域。     

Building a color classification system for textured and hue homogeneous surfaces: system calibration and algorithm

We present a system for classifying the color aspect of textured surfaces having a nearly constant hue (such as wooden boards, textiles, wallpaper, etc.). The system is designed to compensate for small fluctuations (over time) of the light source and for inhomogeneous illumination conditions (shading correction). This is an important feature because even in industrial environments where the lighting conditions are controlled, a constant and homogeneous illumination cannot be guaranteed. Together with an appropriate camera calibration (which includes a periodic update), our approach offers a robust system which is able to “distinguish” (i.e., classify correctly) between surface classes which exhibit visually barely perceptible color variations. In particular, our approach is based on relative (not absolute) color measurements. In this paper, we outline the classification algorithm while focusing in detail on the camera calibration and a method for compensating for fluctuations of the light source. Received: 1 September 1998 / Accepted: 16 March 2000     

用于低照度图像增强的自适应颜色保持算法

视频监控、场景恢复等领域中低照度图像噪点多,亮度低,可视效果差,而现有的图像处理技术容易出现颜色失真、光晕色块严重。为解决这一问题,根据韦伯-费希纳定律,把图像的像素点转换到对数空间,自适应获得符合视觉系统特点的预增强图像;再根据多尺度视网膜算法,分别计算与增强图像的R通道在三个尺度上的平均高斯滤波结果,获得入射光估计,把对数域的自适应增强图像像素值与入射光估计的差值作为多尺度视网膜算法的结果图像。进一步处理结果图像,将其RGB通道按照预增强图像中的颜色比例关系映射到0~255的范围;最后融合三个通道获得最终图像输出。通过图像质量的评价对比,该算法对不同低照度场景图像的增强结果,在对比度、色度保持等方面优于MSR、MSRCR和MSRCP算法。实验证明该算法在低照度图像的恢复和色度保留等方面有较好的效果,在增强视频监控的有效性等方面有较好的应用价值。     

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.