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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01 Jan 2013
TL;DR: This paper presents a review of histogram techniques for image contrast enhancement and the major difference among the methods is only the criteria used to divide the input histogram.
Abstract: The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to providebetter' input for other automated image processing techniques. Histogram equalization (HE) is one of the effective & simple technique for enhancing image quality. However, the conventional histogram equalization methods usually result in excessive contrast enhancement. This paper presents a review of histogram techniques for image contrast enhancement. The major difference among the methods is only the criteria used to divide the input histogram.
27 citations
01 Jan 2013
TL;DR: Histogram Equalization is used for preprocessing of the images and feature extraction process and neural network classifier to check the state of a patient in its early stage whether it is normal or abnormal and the survival rate of a patients by extracted features is predicted.
Abstract: The early detection of lung cancer is a challenging problem, due to the structure of the cancer cells, where most of the cells are overlapped with each other. Classification is very important part of digital image analysis. It is a computational procedure that sort images into groups according to their similarities. In this paper Histogram Equalization is used for preprocessing of the images and feature extraction process and neural network classifier to check the state of a patient in its early stage whether it is normal or abnormal. After that we predict the survival rate of a patient by extracted features. Experimental analysis is made with dataset to evaluate the performance of the different classifiers. The performance is based on the correct and incorrect classification of the classifier. All experiments are conducted in WEKA data mining tool.
27 citations
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TL;DR: It is advocated that a modified fuzzy logic method elucidated in this paper is well suited for contrast enhancement of low-contrast satellite images of the ocean.
Abstract: In this paper, we evaluate the conventional contrast enhancement techniques [histogram equalization (HE), adaptive HE] and the recent gray-level grouping method and the fuzzy logic method in order to find out which of these is well suited for automatic contrast enhancement for satellite images of the ocean, obtained from a variety of sensors. All the techniques evaluated were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality and the Tenengrad criterion. The inter comparison of different techniques was carried out on a standard low-contrast image and also three different satellite images with different characteristics. Based on our study, we advocate that a modified fuzzy logic method elucidated in this paper is well suited for contrast enhancement of low-contrast satellite images of the ocean.
27 citations
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TL;DR: The experiment results show that this method not only can effectively improve the contrast of dust image, but also enhance the details of edge information and get a good visual effect of the image.
Abstract: Basically, the sharpness and contrast of the video images captured in dusty weather will be significantly degraded and diminished. This paper proposes a novel image enhancement method. First convert the degraded image into fuzzy domain to global PAL fuzzy enhancement; then band-limited histogram equalization is adopted for enhancing the local component in the spatial domain; finally POSHE algorithm is introduced to enhance the details. The experiment results show that this method not only can effectively improve the contrast of dust image, but also enhance the details of edge information and get a good visual effect of the image.
27 citations