Histogram equalization

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

Histogram-based image hashing scheme robust against geometric deformations

Abstract: In this paper, we propose a robust image hash algorithm by using the invariance of the image histogram shape to geometric deformations. Robustness and uniqueness of the proposed hash function are investigated in detail by representing the histogram shape as the relative relations in the number of pixels among groups of two different bins. It is found from extensive testing that the histogram-based hash function has a satisfactory performance to various geometric deformations, and is also robust to most common signal processing operations thanks to the use of Gaussian kernel low-pass filter in the preprocessing phase.

Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

Abstract: A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms.

Pixelation effect removal from fiber bundle probe based optical coherence tomography imaging.

Abstract: A method of eliminating pixelization effect from en face optical coherence tomography (OCT) image when a fiber bundle is used as an OCT imaging probe is presented. We have demonstrated that applying a histogram equalization process before performing a weighted-averaged Gaussian smoothing filter to the original lower gray level intensity based image not only removes the structural artifact of the bundle but also enhances the image quality with minimum blurring of object’s image features. The measured contrast-to-noise ratio (CNR) for an image of the US Air Force test target was 14.7dB (4.9dB), after (before) image processing. In addition, by performing the spatial frequency analysis based on two-dimensional discrete Fourier transform (2-D DFT), we were able to observe that the periodic intensity peaks induced by the regularly arrayed structure of the fiber bundle can be efficiently suppressed by 41.0dB for the first nearby side lobe as well as to obtain the precise physical spacing information of the fiber grid. The proposed combined method can also be used as a straight forward image processing tool for any imaging system utilizing fiber bundle as a high-resolution imager.

Brightness preserving weight clustering histogram equalization

Abstract: Histogram equalization (GHE) is a simple and widely accepted method for contrast enhancement. Although there are extensions of GHE that can preserve the brightness of the original image better than the original method, these extensions sometimes fail to enhance the visualization of the original image. Therefore, we propose a new method called "Brightness Preserving Weight Clustering Histogram Equalization" (BPWCHE) that can simultaneously preserve the brightness of the original image and enhance visualization of the original image. BPWCHE assigns each non-zero bin of the original image's histogram to a separate cluster, and computes each cluster's weight. Then, to reduce the number of clusters, we use three criteria (cluster weight, weight ratio and widths of two neighboring clusters) to merge pairs of neighboring clusters. The clusters acquire the same partitions as the result image histogram. Finally, transformation functions for each cluster's sub-histogram are calculated based on the traditional GHE method in the new acquired partitions of the result image histogram, and the sub-histogram's gray levels are mapped to the result image by the corresponding transformation functions. We showed experimentally that BPWCHE can preserve image brightness and enhance visualization of images more effectively than GHE and other brightness preserving methods.

Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation

Abstract: Color image segmentation is a very emerging topic for image processing research. Since it has the ability to present the result in a way that is much more close to the human eyes perceive, so today's more research is going on this area. Choosing a proper color space is a very important issue for color image segmentation process. Generally L*A*B* and HSV are the two frequently chosen color spaces. In this paper a comparative analysis is performed between these two color spaces with respect to color image segmentation. For measuring their performance, we consider the parameters: mse and psnr . It is found that HSV color space is performing better than L*A*B*.