Fast Color Space Transformations Using Minimax Approximations

Color space transformations are frequently used in image processing, graphics, and visualization applications. In many cases, these transformations are complex nonlinear functions, which prohibits their use in time-critical applications. In this paper, we present a new approach called Minimax Approximations for Color-space Transformations (MACT).We demonstrate MACT on three commonly used color space transformations. Extensive experiments on a large and diverse image set and comparisons with well-known multidimensional lookup table interpolation methods show that MACT achieves an excellent balance among four criteria: ease of implementation, memory usage, accuracy, and computational speed

Performance of Basic Vector Directional Filters According to Used Angle Distance

Color images are typical examples of vector-valued signals. For that reason, vector processing represents an optimal approach. Although widely used vector filter is a vector median based on a reduced ordering, the directional processing with utilizing of the angle between input vectors can be used, too. By this way can be achieved well estimates, since vector directional filters preserve color chromaticity, whereas vector median filters may not satisfy this requirement. So, this paper is focused on the performance of basic vector directional filter in dependence on the various angle distances

Color Histogram Equalization using Probability Smoothing

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Color Image Enhancement Method Based on Weighted Image Guided Filtering

A novel color image enhancement method is proposed based on Retinex to enhance color images under non-uniform illumination or poor visibility conditions. Different from the conventional Retinex algorithms, the Weighted Guided Image Filter is used as a surround function instead of the Gaussian filter to estimate the background illumination, which can overcome the drawbacks of local blur and halo artifact that may appear by Gaussian filter. To avoid color distortion, the image is converted to the HSI color model, and only the intensity channel is enhanced. Then a linear color restoration algorithm is adopted to convert the enhanced intensity image back to the RGB color model, which ensures the hue is constant and undistorted. Experimental results show that the proposed method is effective to enhance both color and gray images with low exposure and non-uniform illumination, resulting in better visual quality than traditional method. At the same time, the objective evaluation indicators are also superior to the conventional methods. In addition, the efficiency of the proposed method is also improved thanks to the linear color restoration algorithm.Comment: 15 page

A Study on the Image Recognition Algorithm and Control Module Implementation using Fuzzy Inference

Fuzzy inference is an important issue in research on the fuzzy set theory. Because of the suitability for representing uncertain values, fuzzy numbers have been widely used in many applications. In this paper proposed detecting specific position of the object using fuzzy inference and image processing. Recently instead of classify the object by shade using existing analog sensor, apply a theory to detect of the object information by using image data from CCD camera. It takes interest in the morphology of image, fuzzy inference and image analysis system on knowledge base. and now days, image processing method was used in the most part of field. So, in this paper proposed detecting method on specific position of specific object(fish) about real time gray level input image using image processing method(histogram analysis, binary, erosion, dilation, projection) and application case in intelligent processing system using the fuzzy inference algorithm and based on the computer with machine vision system. We obtained dynamic threshold value, contrast value, brightness value ,erosion and dilation value using fuzzy inference. This processing system is inspected by IBM-PC interface digital I/O card, image grabber board, micro-controller and sensor, etc, so the operating data(the position of cutter, cutting operation, conveyor of movement, detecting fish, observation of sensor data, transmission of cutting data, etc) of processing system are monitoring and control on IBM-PC monitor during the processing time. A view of a component control machine in this paper, one frame image as captured by CCD camera of effective pixel resolution 320×240. If the positioning of the carriage is based on accurate gauging of the desired position, the image contains the necessary information. Finally, a control system processing work-cell was presented as a system to which the model could be applied. The results of a practical implementation of the system were given. We conformed that our study had better performance than conventional one.목차 Abstract = i 제1장 서론 = 1 제2장 퍼지이론 = 3 2.1 보통집합과 퍼지집합 및 연산 = 3 2.2 퍼지추론 = 7 2.3 퍼지 제어기의 구성 = 9 제3장 비젼 시스템 및 영상처리 기법 = 13 3.1 비젼 시스템의 구성 = 13 3.2 영상처리 기법 = 15 제4장 퍼지 추론을 이용한 영상인식 알고리즘 = 20 4.1 퍼지 추론에 의한 임계값 추출 = 20 4.2 퍼지 추론에 의한 명도값 및 대비값 추출 = 29 4.3 퍼지 추론에 의한 영상의 침식과 팽창연산 = 33 제5장 제어모듈 구현 및 실험 = 38 5.1 영상획득 및 처리부 = 38 5.2 제어부 및 구동부 = 40 5.3 실험 및 결과 = 43 제6장 결론 = 52 참고문헌 = 53 부록 = 5

Wavelet-Based Enhancement Technique for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of color digital images based on wavelet transform domain are investigated in this dissertation research. In this research, a novel, fast and robust wavelet-based dynamic range compression and local contrast enhancement (WDRC) algorithm to improve the visibility of digital images captured under non-uniform lighting conditions has been developed. A wavelet transform is mainly used for dimensionality reduction such that a dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients which are obtained by low-pass filtering and down-sampling the original intensity image. The normalized approximation coefficients are transformed using a hyperbolic sine curve and the contrast enhancement is realized by tuning the magnitude of the each coefficient with respect to surrounding coefficients. The transformed coefficients are then de-normalized to their original range. The detail coefficients are also modified to prevent edge deformation. The inverse wavelet transform is carried out resulting in a lower dynamic range and contrast enhanced intensity image. A color restoration process based on the relationship between spectral bands and the luminance of the original image is applied to convert the enhanced intensity image back to a color image. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some pathological scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for tackling the color constancy problem. The illuminant is modeled having an effect on the image histogram as a linear shift and adjust the image histogram to discount the illuminant. The WDRC algorithm is then applied with a slight modification, i.e. instead of using a linear color restoration, a non-linear color restoration process employing the spectral context relationships of the original image is applied. The proposed technique solves the color constancy issue and the overall enhancement algorithm provides attractive results improving visibility even for scenes with near-zero visibility conditions. In this research, a new wavelet-based image interpolation technique that can be used for improving the visibility of tiny features in an image is presented. In wavelet domain interpolation techniques, the input image is usually treated as the low-pass filtered subbands of an unknown wavelet-transformed high-resolution (HR) image, and then the unknown high-resolution image is produced by estimating the wavelet coefficients of the high-pass filtered subbands. The same approach is used to obtain an initial estimate of the high-resolution image by zero filling the high-pass filtered subbands. Detail coefficients are estimated via feeding this initial estimate to an undecimated wavelet transform (UWT). Taking an inverse transform after replacing the approximation coefficients of the UWT with initially estimated HR image, results in the final interpolated image. Experimental results of the proposed algorithms proved their superiority over the state-of-the-art enhancement and interpolation techniques