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 2018TL;DR: This chapter presents Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis system based on image analysis to identify the cells ALL by segmenting each cell in the microscopic images, and then classify each segmented cell to be normal or affected.
Abstract: Leukemia is a kind of cancer that basically begins in the bone marrow. It is caused by excessive production of leukocytes that replace normal blood cells. This chapter presents Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis system based on image analysis. It presented to identify the cells ALL by segmenting each cell in the microscopic images, and then classify each segmented cell to be normal or affected. A well-known dataset was used in this chapter (ALL-IDB2). The dataset contains 260 cell images: 130 normal and 130 affected by ALL. The proposed system starts by segmenting the white blood cells. This process includes sub-processes such as conversion from RGB to CMYK color model, histogram equalization, thresholding by Zack technique, and background removal operation. Then some features were extracted from each cell, each of them represents aspects of a cell. The extracted features include color, texture, and shape features. Then each feature set was exposed to three data normalization techniques z-score, min-max, and grey-scaling to narrow down the gap between the features values. Finally, different classifiers were used to validate the proposed system. The proposed diagnosing system achieved acceptable accuracies when tested by well-known classifiers; however, K-NN achieved the best classification accuracy.
85 citations
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17 Dec 2008TL;DR: An optimum histogram pair based image lossless data embedding scheme using integer wavelet transform and adaptive histogram modification can achieve the highest visual quality of marked image for a given payload as compared with the prior arts of image lossed data hiding.
Abstract: This paper presents an optimum histogram pair based image lossless data embedding scheme using integer wavelet transform and adaptive histogram modification. This new scheme is characterized by (1) the selection of best threshold T , which leads to the highest PSNR of the marked image for a given payload, (2) the adaptive histogram modification, which aims at avoiding underflow and/or overflow, is carried out only when it is necessary, and treats the left side and the right side of histogram individually, seeking a minimum amount of histogram modification, and (3) the selection of most suitable embedding region, which attempts to further improve the PSNR of the marked image in particular when the payload is low. Consequently, it can achieve the highest visual quality of marked image for a given payload as compared with the prior arts of image lossless data hiding. The experimental results have been presented to confirm the claimed superior performance.
84 citations
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24 Nov 2003TL;DR: This paper presents a simple enhancement rate control mechanism for the histogram equalization, which can be used to perform image processing tasks such as black/white level stretch or automatic brightness control as well as variable rate contrast enhancement.
Abstract: The histogram equalization (HE) is a widely used contrast enhancement method. But what is missing from the HE is a mechanism to control the rate of enhancement. The enhanced image always follows the uniform distribution. This paper presents a simple enhancement rate control mechanism for the HE. The gradient of the mapping function is controlled by putting constraints on the probability density function with the bin underflow (BU) and bin overflow (BO). The BUBO operation can provide the rate of enhancement from non to the full HE with a single parameter. With the enhancement rate control mechanism available, the HE can be used to perform image processing tasks such as black/white level stretch or automatic brightness control as well as variable rate contrast enhancement.
83 citations
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TL;DR: A novel adaptive region-based image preprocessing scheme that enhances face images and facilitates the illumination invariant face recognition task, and is shown to be more suitable for dealing with uneven illuminations in face images.
Abstract: Variable illumination conditions, especially the side lighting effects in face images, form a main obstacle in face recognition systems. To deal with this problem, this paper presents a novel adaptive region-based image preprocessing scheme that enhances face images and facilitates the illumination invariant face recognition task. The proposed method first segments an image into different regions according to its different local illumination conditions, then both the contrast and the edges are enhanced regionally so as to alleviate the side lighting effect. Different from existing contrast enhancement methods, we apply the proposed adaptive region-based histogram equalization on the low-frequency coefficients to minimize illumination variations under different lighting conditions. Besides contrast enhancement, by observing that under poor illuminations the high-frequency features become more important in recognition, we propose enlarging the high-frequency coefficients to make face images more distinguishable. This procedure is called edge enhancement (EdgeE). The EdgeE is also region-based. Compared with existing image preprocessing methods, our method is shown to be more suitable for dealing with uneven illuminations in face images. Experimental results on the representative databases, the Yale B+Extended Yale B database and the Carnegie Mellon University-Pose, Illumination, and Expression database, show that the proposed method significantly improves the performance of face images with illumination variations. The proposed method does not require any modeling and model fitting steps and can be implemented easily. Moreover, it can be applied directly to any single image without using any lighting assumption, and any prior information on 3-D face geometry.
83 citations
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30 Oct 1995TL;DR: In this article, the histogram is divided into clusters using a pattern matching technique and then histogram equalization or stretching is performed on each cluster to produce a modified histogram.
Abstract: A method of operating a computer to produce contrast enhanced digital images commences with the step of producing a histogram of having a first axis corresponding to a measurable property (e.g., luminance) and a second axis corresponding to a count of pixels having a particular value for the measurable property. This histogram is divided into clusters and histogram equalization or stretching is performed on each cluster thereby producing a modified histogram. Using said modified histogram to adjust the value of said first measurable property in said digital form, thereby producing a contrast enhanced image. The histogram is divided into clusters using a pattern matching technique. For example, patterns in the histogram that resemble gaussian distributions and patterns that resemble uniform distributions are separated into individual clusters.
83 citations