Histogram equalization

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

An Experiment-Based Review of Low-Light Image Enhancement Methods

Abstract: Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

Gray-level Image Enhancement By Particle Swarm Optimization

Abstract: Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses local and global information of the image. Here an objective criterion for measuring image enhancement is used which considers entropy and edge information of the image. We tried to achieve the best enhanced image according to the objective criterion by optimizing the parameters used in the transformation function with the help of PSO. Results are compared with other enhancement techniques, viz. histogram equalization, contrast stretching and genetic algorithm based image enhancement.

A Contrast Enhancement Framework with JPEG Artifacts Suppression

Abstract: Contrast enhancement is used for many algorithms in computer vision. It is applied either explicitly, such as histogram equalization and tone-curve manipulation, or implicitly via methods that deal with degradation from physical phenomena such as haze, fog or underwater imaging. While contrast enhancement boosts the image appearance, it can unintentionally boost unsightly image artifacts, especially artifacts from JPEG compression. Most JPEG implementations optimize the compression in a scene-dependent manner such that low-contrast images exhibit few perceivable artifacts even for relatively high-compression factors. After contrast enhancement, however, these artifacts become significantly visible. Although there are numerous approaches targeting JPEG artifact reduction, these are generic in nature and are applied either as pre- or post-processing steps. When applied as pre-processing, existing methods tend to over smooth the image. When applied as post-processing, these are often ineffective at removing the boosted artifacts. To resolve this problem, we propose a framework that suppresses compression artifacts as an integral part of the contrast enhancement procedure. We show that this approach can produce compelling results superior to those obtained by existing JPEG artifacts removal methods for several types of contrast enhancement problems.