基于视觉特性的非线性多尺度彩色图像增强

[目的]针对图像在低光照下的亮度和对比度偏低的问题,提出一种基于视觉特性的非线性多尺度彩色图像增强算法.[方法]该算法将彩色图像从RGB色彩空间转化到HSI色彩空间,保持H分量不变,对S分量进行指数拉伸,对Ⅰ分量利用视觉系统模型和非线性映射方法实现图像对比度增强,再通过自适应的亮度调整增加图像的全局亮度.最后将HSI色彩空间转化到RGB色彩空间,从而实现对彩色图像自适应增强.[结果]通过对低光照彩色图像进行增强测试,其测试结果表明,[结论]该算法能够自适应地调整图像的全局亮度,增加图像的局部细节对比度,并保持其原色彩,提升彩色图像在低光照下的视见度.     

基于自适应权重Retinex和小波变换的彩色图像增强算法

针对现有图像增强技术容易出现细节丢失、局部曝光不足、过曝光或颜色失真,不能兼顾对比度和色彩保真的问题,提出了基于自适应权重Retinex与小波变换结合的彩色图像增强算法(AMSR-WT)。将图像从RGB空间转换到HSI空间,对亮度分量I进行小波变换分解为低频亮度图像和若干高频亮度图像,对低频图像使用自适应权重Retinex进行增强,对高频图像使用改进的阈值去噪算法进行去噪,通过小波逆变换重构亮度分量,经过Gamma校正进一步增强对比度并转换回RGB空间得到增强图像。实验结果表明,该算法有效提高了图像对比度和颜色保真度,较好地保留了图像的细节和纹理。     

基于模糊理论和CLAHE的雾天图像自适应清晰化算法

为了解决雾天图像低对比度的问题,提出了一种基于模糊理论和CLAHE的雾天图像的自适应清晰化算法.此算法结合图像的均值和标准差,将雾天图像从空域转换到模糊域,采用模糊增强算法实现全局雾天图像的自适应对比度增强后再采用有约束的局部直方图算法对雾天图像的亮度分量进行处理,在空域内进一步实现雾天图像的对比度增强.实验结果表明,该算法将模糊域和空间域的方法相结合,可以提高雾天图像的亮度和对比度,使雾天图像的视觉效果得到一定改善.     

自适应高斯融合双通道的去雾算法

针对传统图像去雾算法存在的对比度下降和颜色偏移等问题,提出一种结合高斯融合的自适应双通道雾霾图像复原算法。首先,考虑到大气光应小于有雾图像最大值,且大于有雾图像最小值,根据亮度控制因子自适应控制的方式得到融合中通道,并获得中通道下的局部大气光;其次,提出双通道线性传输,即用最大值通道辅助完成线性传输,再用高斯函数加权融合的方法实现清晰图像最优通道估计,从而得到最优透射率;最后,结合复原模型恢复清晰图像。实验表明,所提方法有效解决了图像对比度下降与颜色偏移等问题,去雾效果良好、亮度适宜且颜色保真度更高。另外,该方法在定量指标上同样具有优越的表现。      

自适应HSV空间Retinex煤矿监控图像增强算法

针对煤矿井下的监控图像由于粉尘、煤尘、低照度或点光源等光照的影响,而整体阴暗模糊,对比度低,背景噪声强,视觉效果不理想的问题,提出一种改进的HSV(Hue,Saturation,Value)空间的颜色可恢复的多尺度Retinex(Muhi-Scale Ret-inex,MSR)的图像增强算法.该算法先将图像从RGB空间转换到HSV空间,以确保后续图像增强处理不会影响图像的色彩效果;然后利用提出的自适应的高斯核函数分离亮度分量V的照度分量和反射分量,再利用自适应的增益系数对反射分量进行增强处理,获得不受光照影响的、增强的反射图像;最后将反射图像逆变换回RGB空间,再利用优化的颜色恢复函数对增强的图像进行颜色修正,从而改善图像的全局视觉效果和局部对比度.实验表明该方法能有效提高煤矿井下监控图像的对比度和亮度,抑制背景噪声,从而大大改善煤矿井下监控图像的视觉效果,且很好地实现了参数自适应,减少了人为因素对结果的影响.     

基于双边滤波的单尺度Retinex图像增强算法

提出了一种基于双边滤波的单尺度Retinex图像增强方法,在有效增强图像的同时,很好地保持了图像边缘及高亮部分的细节.采用双边高斯滤波来估计图像亮度,同时对原始图像进行局部对比度增强,有效地消除了光晕现象并增强了局部对比度.通过Weber定律在增强过程中对图像亮度进行控制,防止增强后的图像过亮,保持图像高亮部分的细节.实验结果表明:该方法增强效果明显,适用于灰度图像和彩色图像,并能防止颜色失真,使灰度和彩色图像达到较好的视觉效果.     

基于辅助变量增强的可逆彩色图像灰度化

彩色图像灰度化是一种被广泛应用于各个领域的图像压缩方式，但很少有研究关注彩色图像与灰度图像之间的相互转换技术。该文运用深度学习，创新性地提出了一种基于辅助变量增强的可逆彩色图像灰度化方法。该方法使用变量增强技术来保证输出与输入变量通道数相同以满足网络的可逆特性。具体来说，该方法通过可逆神经网络的正向过程实现彩色图像灰度化，逆向过程实现灰度图像的色彩复原。将所提方法在VOC2012, NCD和Wallpaper数据集上进行定性和定量比较。实验结果表明，所提方法在评价指标上均获得了更好的结果。无论是在全局还是局部，生成图像都可以最大程度地保留亮度、颜色对比度和结构相关性等特征。     

基于视觉感受野的夜间彩色图像自适应增强算法

针对夜间环境造成的图像对比度下降和增强过程中带来的噪声、过曝和光晕效应等问题,提出了一种基于视觉感受野的夜间彩色图像自适应增强算法。首先将图像转换到HSV空间,鉴于视觉感知的对比度敏感特性,利用中心兴奋区及外周抑制区组成的拮抗式同心圆双高斯差(DOG)感受野模型对图像亮度空间V进行对比度自适应调节,并对模型参数σ1、σ2和局部对比度都进行量化水平为4的均匀量化,局部对比度较高的量化区间采用较小模型参数,局部对比度较低的量化区间采用较大的模型参数,模型参数A2与局部对比度成反比进行微调,最后将图像转换到回RGB空间。通过与流行算法MSRCR、NPE及LIME的对比实验表明,该算法获得了较好的主观视觉效果,且在对比度、亮度提高的同时避免了暗区域噪声过大,并更好地增强了轮廓信息。     

图像通信中一种低照度彩色图像增强算法

针对低照度彩色图像细节模糊、亮度不高等问题,提出一种新的彩色图像增强算法。首先引入新传递函数改进传统同态滤波,然后,在RGB色彩空间上,分别对R、G、B分量用改进的同态滤波和对比度受限自适应直方图均衡（CLAHE）进行增强。接着,转换到HSV色彩空间,用非线性函数对亮度进行光照补偿,对饱和度进行1.5倍拉伸。最后恢复图像色彩信息。实验结果表明,新算法在保持图像细节的同时能够增强图像对比度,使图像清晰度更高。     

基于改进Retinex算法增强夜视融合图像的颜色传递

颜色传递是获得夜视图像自然彩色的一种方法。 本文利用颜色传递技术得到红外与微光融合 图像的自然彩色,在采用匹配邻域亮度均值和标准差颜色传递算法的基础上,提出了通过增 强夜视融合图 像的亮度-对比度来提高匹配精度,减小传统算法中由于亮度接近导致在颜色传递过程中的 误传现象。首先 利用像素平均法将红外和微光图像进行灰度融合;然后采用提出的改进多尺度Retinex增强 算法将融合图像 进行增强;最后在YCbCr颜色空间匹配灰度融合图像与参考彩色 图像亮度的均值和标准差,应用颜色传递 技术得到彩色夜视图像。实验结果表明,本文采用改进的多尺度Retinex算法使融合图像的 亮度-对比度得 到显著的提升,经过颜色传递后得到的彩色图像纹理细节清晰,目标背景对比度高,具有和 参考图像相近的真彩色。     

Automatic color image contrast enhancement using Gaussian mixture modeling,piecewise linear transformation,and monotone piecewise cubic interpolant

Conventional contrast enhancement algorithms use complicated nonlinear mapping functions with parameters specified manually. In this paper, a parameter-free piecewise linear transformation and monotone piecewise cubic interpolation are used for color image contrast enhancement. The Gaussian mixture model is used to divide luminance histogram of input color image into multiple sub-histograms. Each sub-histogram is mapped linearly or nonlinearly to a portion of available output dynamic range proportional to its extent and the number of its pixels. The proposed algorithm has more controlled contrast enhancement than conventional algorithms to preserve natural outlook and local details in the input color image. This is done by considering brightness histogram preservation of input image by proposed algorithm, due to considering extent of each sub-histogram for allocating a portion of output dynamic range to it. Experimental results show that the proposed algorithm produces better or comparable contrast-enhanced images with natural outlook than several conventional algorithms on various types of images.     

红外中波细分图像的伪彩色增强

提出了基于lαβ颜色空间变换和小波包变换的红外中波细分波段图像的伪彩色增强方法.利用lαβ变换将两个细分波段RGB彩色映射的伪彩色图像的颜色和亮度信息分离,通过二维小波包变换得到两个细分波段的融合图像,将融合图像和颜色信息经过lαβ逆变换形成伪彩色增强图像.实验结果表明,同简单彩色映射图像相比,伪彩色增强图像的局部标准偏差和局部熵都有增加,从而使图像具有更好的感知效果.     

基于S曲线全局动态调光的改进算法

S曲线全局动态调光算法可以降低LED液晶显示器的功耗,同时能够提高显示图像的静态对比度,但该算法会造成部分图像色彩失真和细节丢失。针对这一问题,本文提出一种图像细节层分离与视觉显著性理论相结合的S曲线改进算法。首先,将原始图像转换至HSV色彩空间进行亮度和色度分离;然后,在图像亮度分量上采用双边滤波得到图像的基础层与细节层,基础层采用S曲线进行动态范围拉伸,实现像素补偿,细节层则运用视觉显著性理论进行分区与权值增强,弥补由像素补偿带来的细节损失;最后,将处理后的各层图像转换至RGB空间显示。将本文算法的仿真结果与原S曲线算法的结果进行对比。结果显示,本文算法在维持原算法功耗降低和静态对比度提升水平不变的基础上,解决了原算法在部分图像中出现的色彩失真和细节丢失问题,提升了图像的视觉显示效果,同时本文算法的仿真结果具有更大的信息熵和平均梯度。     

Color contrast enhancement method using steerable pyramid transform

A new method of contrast enhancement based on steerable pyramid transform is presented in this work. The use of steerable filters is motivated by the fact that the images are to be observed by human and therefore it would be better to incorporate some knowledge on the Human Visual System in the design of the image processing tool. Here, the frequency and directional selectivity of the HVS is modeled by the steerable filters. The contrast is amplified using a selective nonlinear function which simulates the nonlinearity response of the HVS to the luminance stimuli. So the basic idea is to enhance the luminance signal irrespective of the two chrominance components using a multidirectional and multiscale decorrelation color transform. Initially the rgb (red, green and blue) color image is converted to lab (luminance and chrominance) color image. Only the luminance component is transformed by the steerable pyramid transform, so that the luminance component is independently decomposed into different scale and orientation sub-bands. The contrast in each sub-band is enhanced using a nonlinear mapping function. Finally the rgb color image is obtained from the enhanced luminance component along with the original chrominance components. The performance of the proposed method is objectively evaluated using spectrum energy analysis and a visibility map based on a perceptual filtering model. The obtained results confirm the efficiency of the method in enhancing subtle details without affecting color balance and without the usual noise amplification and edge ringing effect.     

两种不同类型的滤光片式彩色亮度计性能比较

显示器的色度和亮度可以通过彩色亮度计进行测量。小视场滤光片式彩色亮度计因其低成本在生产线上应用广泛,而近来出现的影像式彩色亮度计虽然也属滤光片式,但是由于其探测器不同而具有大视场,且结构、原理和性能与前者都有差异。量化比较这两类滤光片式彩色亮度计的性能,可以为影像式彩色亮度计的质量评估提供依据。文章分别选取两类滤光片式彩色亮度计中的代表型号,通过进行原理分析和实验验证进行对比分析。结果表明,影像式彩色亮度计的性能不低于小视场滤光片式彩色亮度计。     

Background adjustment and saturation enhancement in ancient Chinese paintings.

This work presents a color enhancement scheme to virtually restore ancient Chinese paintings in electronic form. Two degradations result in color contrast loss in ancient Chinese paintings: paper aging and pigment fading. The proposed enhancement scheme comprises two subsequent methods: background adjustment and saturation enhancement. The former method, based on the Von Kries color conversion in the CIE xyY color space, retrieves the original color of the paint paper by modifying colors based on their similarity to the background color. The proposed saturation enhancement method makes colors more vivid and bright, and also improves the image contrast.     

Perceptual color correction through variational techniques.

In this paper, we present a discussion about perceptual-based color correction of digital images in the framework of variational techniques. We propose a novel image functional whose minimization produces a perceptually inspired color enhanced version of the original. The variational formulation permits a more flexible local control of contrast adjustment and attachment to data. We show that a numerical implementation of the gradient descent technique applied to this energy functional coincides with the equation of automatic color enhancement (ACE), a particular perceptual-based model of color enhancement. Moreover, we prove that a numerical approximation of the Euler-Lagrange equation reduces the computational complexity of ACE from theta(N2) to theta(N log N), where N is the total number of pixels in the image.     

Low light image enhancement with dual-tree complex wavelet transform

In low light condition, low dynamic range of the captured image distorts the contrast and results in high noise levels. In this paper, we propose an effective contrast enhancement method based on dual-tree complex wavelet transform (DT-CWT) which operates on a wide range of imagery without noise amplification. In terms of enhancement, we employ a logarithmic function for global brightness enhancement based on the nonlinear response of human vision to luminance. Moreover, we enhance the local contrast by contrast limited adaptive histogram equalization (CLAHE) in low-pass subbands to make image structure clearer. In terms of noise reduction, based on the direction selective property of DT-CWT, we perform content-based total variation (TV) diffusion which controls the smoothing degree according to noise and edges in high-pass subbands. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancment and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.     

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.