Histogram equalization

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

Adaptive histogram equalization for images with strong local contrast

Abstract: The present invention relates to a method, apparatus and computer program product for contrast enhancement of images based on adaptive histogram equalization In particular it relates to preventing adaptive histogram equalization from causing fading artifacts and object extension artifacts An adaptive histogram equalization method is provided comprising the steps of dividing an image into regions of pixels, determining structures of local pixel value differences of a predefined strength of the image, building for every region a histogram of the pixel values based on the determined structures of local pixel value differences and mapping pixel values of each region based on the histogram corresponding to the region

Digital Image Processing with Application to Digital Cinema

Abstract: 1. Introduction 2. Two-Dimensional Signal, Systems, and Discrete Fourier Transform 3. Human Visual Perception 4. Image Acquisition 5. Image Enhancement 6. Image Transforms 7. Basic Principles of Image Compression 8. Video Compression Standards 9. Digital Cinema 10. Microarray Image Processing Appx A: Fourier Transform Appx B: Definitions and Units of Photometric Quantities Appx C: Derivation of Histogram Equalization Appx D: Basics of Information Theory

Remote sensing image enhancement based on the combination of nonsubsampled shearlet transform and guided filtering

Abstract: Aiming at the characteristics of remote sensing images with low-contrast, weak edge preservation, and poor resolution textual information, an image enhancement method that combines nonsubsampled shearlet transform (NSST) and guided filtering is presented. First, histogram equalization is applied to the remote sensing image. Second, the image is decomposed into a low frequency component and several high frequency components by NSST. Then, a linear stretch is adopted for the coefficients of the low-frequency component to improve the contrast of the original image; the threshold method is used to restrain the noise in the high-frequency components, then guided filtering is used for dealing with the high-frequency components, improving the detail information and edge-gradient retention ability. Finally, the final enhanced image is reconstructed by applying the inverse NSST to the processed low- and high-frequency components. The results show that the algorithm can significantly improve the visual impression of the image. Compared with the proposed algorithms in recent years, the average gradient and information entropy are significantly improved and the running time is shortened.

Contrast enhancement for medical images based on histogram equalization followed by median filter

Abstract: The goal of image enhancement technique is to improve a characteristics or quality of an image, such that the resulting image is better than the original image. Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an images, where preserving the original brightness is essential to avoid annoying artifacts. So Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. The extension of BBHE is Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE). The result of MMBEBHE is bad for the image with a lot details. To overcome these drawbacks, a new method is proposed. In this method, image enhancement is performed by MMBEBHE based on a modified contrast stretching manipulation. While the image is enhanced, the impulse noises present in the images are also enhanced. To avoid this effect, the enhanced image is passed through a median filter. The median filter is an effective method for the removal of impulse based noise on images. This is due to the partial averaging effect of the median filter and its biasing of the input stream, rather than straight mathematical averaging. Keywords—Image Enhancement, Contrast stretching, histogram equalization (HE), MMBEBHE, padding, median filter.

A logarithmic law based histogram modification scheme for naturalness image contrast enhancement

Abstract: In this paper, a new logarithmic histogram modification technique for image contrast enhancement with naturalness preservation has been proposed. Traditional histogram equalization scheme usually causes extreme contrast enhancement, which results in unnatural look and artifacts. The proposed technique first enhances the contrast of the image globally through addition and logarithmic law based modification scheme, thereafter the local details of the image are emphasized through the coefficient scaling directly in the compressed domain using discrete cosine transformation. The proposed method can enhance the image contrast uniformly with less number of parameters without losing its basic features. Experimental results show that the proposed method preserves the natural appearance of the image and yields better perceptual quality as compared to the state-of-the-art techniques.