Filter considerations in color correction

The quality of color correction is dependent on the filters used to scan the image. This paper introduces a method of selecting the color filters using a priori information about the viewing illuminants. Color correction results using the derived filters are compared with color correction results using filters that are optimal for individual viewing and recording illuminants. The comparison is performed using the CIE PEL (L*a*b*) perceptual color difference measure. Applications of this work are found in the design of scanners, copiers, and television systems.     

Subpixel rendering for diamond-shaped PenTile displays using patch-based adaptive filters

We propose a novel subpixel rendering algorithm for diamond-shaped PenTile displays, which reduces color distortions while improving apparent resolutions. We develop two types of subpixel rendering filters: main filter and color distortion reduction (CDR) filters. To derive the filters, we formulate a quadratic program to minimize the difference between an original input image and a virtual image that the human visual system perceives. By imposing two constraints for filter size and coefficients, we obtain the main filter, which has a suitable size and is normalized. Then, we design the CDR filters based on the analysis of various patch patterns for image areas. We define the patch patterns to classify local areas with possible color distortions. By imposing additional constraints according to the patch patterns, we derive the CDR filters. Lastly, by matching local areas in the input image into the pre-defined patch patterns, we render the image using the main filter and the CDR filters, which are applied adaptively to the local areas. Experimental results demonstrate that the proposed subpixel rendering algorithm improves apparent resolutions and suppresses color distortions effectively, thereby outperforming conventional algorithms.     

Multispectral Filter-Wheel Cameras: Geometric Distortion Model and Compensation Algorithms

Multispectral image acquisition considerably improves color accuracy in comparison to RGB technology. A common multispectral camera design concept features a filter-wheel consisting of six or more optical bandpass filters. By shifting the filters sequentially into the optical path, the electromagnetic spectrum is acquired through the channels, thus making an approximate reconstruction of the spectrum feasible. However, since the optical filters exhibit different thicknesses, refraction indices and may not be aligned in a perfectly coplanar manner, geometric distortions occur in each spectral channel: The reconstructed RGB images thus show rainbow-like color fringes. To compensate for these, we analyze the optical path and derive a mathematical model of the distortions. Based on this model we present two different algorithms for compensation and show that the color fringes vanish completely after application of our algorithms. We also evaluate our compensation algorithms in terms of accuracy and execution time.      

Design and analysis of vector color error diffusion halftoningsystems

Traditional error diffusion halftoning is a high quality method for producing binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error diffusion may be extended to color images by using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we propose three contributions. First, we analyze vector color error diffusion based on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts the key characteristics of color error diffusion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos (1987) and by Kite et al. (1997) as special cases. Second, based on our model, we optimize the noise shaping behavior of color error diffusion by designing error filters that are optimum with respect to any given linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filter has a parallel implementation as a polyphase filterbank.     

Least squares restoration of multichannel images

Multichannel restoration using both within- and between-channel deterministic information is considered. A multichannel image is a set of image planes that exhibit cross-plane similarity. Existing optimal restoration filters for single-plane images yield suboptimal results when applied to multichannel images, since between-channel information is not utilized. Multichannel least squares restoration filters are developed using the set theoretic and the constrained optimization approaches. A geometric interpretation of the estimates of both filters is given. Color images (three-channel imagery with red, green, and blue components) are considered. Constraints that capture the within- and between-channel properties of color images are developed. Issues associated with the computation of the two estimates are addressed. A spatially adaptive, multichannel least squares filter that utilizes local within- and between-channel image properties is proposed. Experiments using color images are described     

Color image processing using adaptive multichannel filters

New adaptive filters for color image processing are introduced and analyzed. The proposed adaptive methodology constitutes a unifying and powerful framework for multichannel signal processing. Using the proposed methodology, color image filtering problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated, results. The new filters utilize Bayesian techniques and nonparametric methodologies to adapt to local data in the color image. The principles behind the new filters are explained in detail. Simulation studies indicate that the new filters are computationally attractive and have excellent performance.     

彩色形态滤波器分析

形态滤波属于非线性信号变换处理,它利用信号的局部特征对信号进行修正。最近的研究表明形态滤波器能有效地滤除二值图像及灰度图像中的噪声,本文引入了建立在ＲＧＢ彩色空间上的彩色形态变换,针数字形态学理论从灰度空间扩展到彩色空间,对基本彩色形态滤波器（即彩色形态闭、开）及以它们为基础所构造出一些复杂的彩色形态滤波器进行了研究,并对它们选用不同结构元素的情况进行了分析,实验表明彩色形态滤波器能直接有效地消除     

基于加权平均值的汽车车牌监控图像过滤技术研究

车牌监控图像由于照明、天气、运动目标位置和运动目标速度的不同,图像质量差异很大,从而不利于车牌监控的定位和识别。本文采用最大值法、平均值法和加权平均值法三种方法对车牌监控图像进行过滤,结果表明：采用加权平均值法进行车牌图像颜色过滤能够保留绝大部分的汽车车牌信息,使得目标和背景之间边界清晰,是一种较好的车牌图像彩色过滤方法。     

基于离散四元数余弦变换的彩色图像数字水印技术

将离散四元数余弦变换(DQCT)应用于彩色图像的数字水印技术中,提出了一种在彩色图像中嵌入水印的新技术。应用四元数理论把彩色图像作为一整体进行余弦变换,在彩色宿主图像的DQCT域中实现彩色水印图像的嵌入。实验表明,该方法具有较好的抗压缩能力,对高斯噪声、椒盐噪声和缩放攻击具有较好的鲁棒性。     

A new Copy-Move forgery detection method using LIOP

The most prevalent type of digital image falsification occurs when a portion of a image is copied and pasted onto another section of the same image. Falsification of the image made in this way is called copy-move forgery (CMF). This study presents a new and effective approach for copy-move forgery detection (CMFD) using the Local Intensity Order Pattern (LIOP) to overcome the restrictions of existing CMFD techniques. The input image is first converted to a YCbCr color space and then split into Y, Cb, and Cr color channels. The LIOP features are then extracted from each color channel and all the features are combined. The feature vectors are ordered lexicographically and related features are detected by comparing the LIOP features. Although the LIOP feature has rarely been used in CMFD prior to this study, the success rate of the proposed method is high. In addition, since the channels are not correlated to each other in the YCbCr color space, each color channel is considered as a gray image, and the success rate is increased by combining the features extracted from each of the color channels. The proposed approach was assessed using the CoMoFoD and GRIP datasets. Experimental findings demonstrated that the suggested method was successful and displayed robustness in post-processing attacks.     

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.     

A CMOS image sensor with a double-junction active pixel

A CMOS image sensor that employs a vertically integrated double-junction photodiode structure is presented. This allows color imaging with only two filters. The sensor uses a 184*154 (near-QCIF) 6-transistor pixel array at a 9.6-/spl mu/m pitch implemented in 0.35-/spl mu/m technology. Results of the device characterization are presented. The imaging performance of an integrated two-filter color sensor is also projected, using measurements and software processing of subsampled images from the monochrome sensor with two color filters.     

基于信息熵和Gabor滤波器的图像检索

采用颜色和纹理的多特征提取,将图像按照一定的规则进行分块,对各个分块分别进行各种特征向量的提取.采用HSV颜色空间把颜色特征量化到72个颜色空间得到72柄的一维直方图,计算图像信息熵;纹理特征采用Gabor滤波器.该种分块方法能够很好的利用图像内容的空间信息,综合颜色和纹理特征能够有效地提高查全率和差准率.     

Mathematics for demosaicking

Digital color cameras sample the continuous color spectrum using three or more filters; however, each pixel represents a sample of only one of the color bands. This arrangement is called a mosaic. To produce a full-resolution color image, the recorded image must be processed to estimate the values of the pixels for all the other color bands. This restoration process is often called demosaicking. This paper uses stacked notation to represent the mosaicked image capture and derives the minimum mean square error (MMSE) estimator for the demosaicked image. By making common assumptions, the restoration can be computed in a cost-effective manner. Extensions to the linear method are proposed to allow adaptive behavior.     

仅位相型滤波器实现彩色图像的非相干光识别

本文提出了一种用非相干系统识别采色图像的方法,将彩色图像分成三个单色像处理,设计一可实现的仅位相型光学传递函数（ＯＴＦ）,对其进行带通优化,从而提高信噪比并使相关峰锐化,通过一对滤波器的来合成该ＯＴＦ。介绍了滤波器制作原理和方法,获得了彩色图像相关识别的实验结果。     

夜视图像自动彩色化源图选择算法

研究可供夜视图像进行色彩传递的自然彩色源图选择算法。利用Gabor滤波器能够模拟生物视觉的特性以及夜视图像的纹理特征,对结合Gabor滤波器和灰度共生矩阵源图检索算法做出了改进,提出了一种结合Gabor滤波器和灰度梯度共生矩阵的源图选择算法。最后对这两种算法和亮度梯度二次采样算法进行了比较,实验结果证明,改进的算法远远优于其他两个,减少了图像色彩传递应用过程的人工干预程度,大大提高了自动化水平。     

Vector directional filters-a new class of multichannel imageprocessing filters

Vector directional filters (VDF) for multichannel image processing are introduced and studied. These filters separate the processing of vector-valued signals into directional processing and magnitude processing. This provides a link between single-channel image processing where only magnitude processing is essentially performed, and multichannel image processing where both the direction and the magnitude of the image vectors play an important role in the resulting (processed) image. VDF find applications in satellite image data processing, color image processing, and multispectral biomedical image processing. Results are presented here for the case of color images, as an important example of multichannel image processing. It is shown that VDF can achieve very good filtering results for various noise source models.     

Multichannel techniques in color image enhancement and modeling

We present novel multichannel methods in two target research areas. The first area is color image modeling. Multichannel AR models have been developed and applied to color texture segmentation and synthesis. The second area is color image equalization, which is performed on the three RGB channels simultaneously, using the joint PDF. Alternatively, equalization at the HSI domain is performed in order to avoid changes in digital image hue. A parallel algorithm is proposed for color image histogram calculation and equalization     

Image quality assessment based on S-CIELAB model

This paper proposes a new image quality assessment framework which is based on color perceptual model. By analyzing the shortages of the existing image quality assessment methods and combining the color perceptual model, the general framework of color image quality assessment based on the S-CIELAB color space is presented. The S-CIELAB color model, a spatial extension of CIELAB, has an excellent performance for mimicking the perceptual processing of human color vision. This paper incorporates excellent color perceptual characteristics model with the geometrical distortion measurement to assess the image quality. First, the reference and distorted images are transformed into S-CIELAB color perceptual space, and the transformed images are evaluated by existing metric in three color perceptual channels. The fidelity factors of three channels are weighted to obtain the image quality. Experimental results achieved on LIVE database II shows that the proposed methods are in good consistency with human subjective assessment results.