Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

A method of and system for detecting uniform color segments

Abstract: The invention provides a method of detecting segment boundaries for a series of successive frames in a video sequence, including steps of acquiring color information from each frame, determining color histogram for each frame, applying boundary detection technique utilizing color histograms. Said method includes segmenting frames of video sequence into uniform color segments. Additionally, a system is provided for detecting segment boundaries for a series of successive frames in a video sequence. The system includes means for acquiring color information from each frame, means for determining color histogram for each frame, and means for applying boundary detection technique utilizing the color histograms. The system includes means for segmenting frames of video sequence into uniform color segments. Boundary detection techniques include a family color histogram method, weighted average color histogram method, successive color histogram method, stochastic method, shot-based color histogram method, mosaic color histogram method, and a computable macro-segment boundary method.

Bi-histogram equalization using modified histogram bins

Abstract: Display Omitted The proposed BHEMHB improves conventional histogram equalization.Histogram segmentation enables mean brightness preservation.Histogram modification reduces domination effect of high-frequency histogram bins.BHEMHB is tested using standard and cervical cell images.Statistical analyses reveal improvement in entropy, PSNR and AMBE measurements. The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance.

Histogram Equalization: A Strong Technique for Image Enhancement

Abstract: Generally for improving contrast in digital images, HE is the method that commonly used but in result it gives unnatural artifacts like intensity saturation, over-enhancement and noise amplification. To overcome these problems there was a need to partition the image histogram, at first image histogram was partitioned into two parts and then different transformation functions were applied on each partition. After that image histogram was partitioned into many partitions and same process was applied with some additional features. DHE is the multi histogram method and CLAHE is the extension of AHE. These methods are compared to HE and found that both methods give better result than HE but DHE method also gives better result than CLAHE.

Color Image Enhancement Based on Single-Scale Retinex With a JND-Based Nonlinear Filter

Abstract: In this paper, we propose a color image enhancement based on the single-scale retinex (SSR) with a just noticeable difference (JND)-based nonlinear filter. In the proposed method, an input RGB color image is transformed into an HSV color image. Under the assumption of white-light illumination, the S and V component images are enhanced. In the enhancement of the V component image, the illumination is first estimated using the JND-based nonlinear filter. The output V component image is then obtained by subtracting some portion of the log signal of the estimated illumination from the log signal of the input V component image. The histogram modeling is next applied to the output V component image. The S component image is enhanced in proportion to the enhanced ratio of the V component image. Finally an output RGB color image is obtained from the enhanced V and S component images along with the original H component image. Experimental results show that the proposed method yields better performance of color enhancement over the conventional histogram equalization and SSR for test color images.