Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
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TL;DR: Experimental results show that the proposed fuzzy color histogram-based shot-boundary detection algorithm effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot- boundary detection algorithms.
88 citations
Patent•
13 Nov 1992TL;DR: In this article, the histogram of the digital input image is divided into a region of interest, a low-signal foreground region, and a high signal background region and the tonescale is constructed to be substantially linear over the regions of interest and the entire image is subject to certain output density constraints.
Abstract: A method and apparatus for automatically and adaptively generating tonescale transformation functions that are robust with respect to imaging systems, exposure conditions, and body parts. The technique uses the histogram of the digital input image, the cumulative distribution function of the histogram, and the entropy of subsections of the histogram to create the final tonescale transformation. Using these three functions, the histogram can be divided into a region of interest, a low-signal foreground region, and a high-signal background region. The tonescale is constructed to be substantially linear over the region of interest, joining smoothly with a nonlinear portion extending from the end of the low-signal foreground region to the start of the region of interest, and another nonlinear portion of the high-signal background region. The substantially linear region of interest and the entire image are subject to certain output density constraints to optimize the diagnostic utility of the final image. The technique depends entirely on the histogram of the input image, and, hence, it is adaptive and robust.
88 citations
TL;DR: The proposed automatic and parameter-free contrast enhancement algorithm for color images can be run on an embedded environment and processed in real-time system due to its simplicity and efficiently.
Abstract: Conventional contrast enhancement methods have four shortcomings. First, most of them need transformation functions and parameters which are specified manually. Second, most of them are application-oriented methods. Third, most of them are performed on gray level images. Fourth, the histogram equalization (HE) based enhancement methods use non-linear transform function. Thus, this paper proposes an automatic and parameter-free contrast enhancement algorithm for color images. This method includes following steps: First, RGB color space is transformed to HSV color space. Second, image content analysis is used to analyze the image illumination distribution. Third, the original image is enhanced by piecewise linear based enhancement method. Finally, the enhancement image is transformed back to RGB color space. This novel enhancement is automatic and parameter-free. Our experiments included various color images with low and high contrast. Experiment results show that the performance of the proposed method is better than histogram equalization (HE) and its six variations in non-over enhancement and natural clearly revealed. Moreover, the proposed algorithm can be run on an embedded environment (such as mobile device, digital camera, or other consumer products) and processed in real-time system due to its simplicity and efficiently.
87 citations
30 Oct 2009
TL;DR: A Contrast Limited Adaptive Histogram Equalization (CLAHE)-based method that establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray-level can limit the noise while enhancing the image contrast.
Abstract: The images degraded by fog suffer from poor contrast. In order to remove fog effect, a Contrast Limited Adaptive Histogram Equalization (CLAHE)-based method is presented in this paper. This method establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray-level. It can limit the noise while enhancing the image contrast. In our method, firstly, the original image is converted from RGB to HSI. Secondly, the intensity component of the HSI image is processed by CLAHE. Finally, the HSI image is converted back to RGB image. To evaluate the effectiveness of the proposed method, we experiment with a color image degraded by fog and apply the edge detection to the image. The results show that our method is effective in comparison with traditional methods.
87 citations
TL;DR: Several possibilities to extend the method known as brightness preserving dynamic histogram equalization (BPDHE) for color images by maintaining the mean intensity of the input image in the output image are presented.
Abstract: Histogram equalization (HE), although one of the most popular techniques used for digital image enhancement, is not very suitable to be implemented directly in consumer electronics, such as television, because this method tends to produce an output with saturation effect. To overcome this weakness, it is suggested that the mean intensity of the input image be maintained in the output image. Previously, we proposed a method known as brightness preserving dynamic histogram equalization (BPDHE) which can fulfill this requirement for grayscale images. In this paper, we present several possibilities to extend this method for color images.
86 citations