Image Enhancement Techniques


Image enhancement can be considered as one of the fundamental processes in image analysis. The goal of contrast enhancement is to improve the quality of an image to become more suitable for a particular application. Till today, numerous image enhancement methods have been proposed for various applications and efforts have been directed to further increase the quality of the enhancement results and minimize the computational complexity and memory usage. This paper presents an exhaustive review of image enhancement methods and suggests a direction for future developments of image enhancement methods.

Keywords: Digital Image Processing, Geometric Corrections, Gray Scale Manipulation, Image Enhancement.


The image enhancement technique is to make the digital picture more appealing to our eyes, for example, making the images smooth or sharp. This is an important topic in digital image processing. It can help humans and computer vision algorithms obtain accurate information from the enhanced images. The visual quality and certain image properties, such as brightness, contrast, signal-to-noise ratio, resolution, edge sharpness, and color accuracy were improved through the enhancement process. Recently, many image enhancement methods have been developed based on various digital image processing techniques and applications. The enhanced image will provide useful information for post-processing, especially in segmentation stage Image enhancement is used in every field place for example, medical representation study, analysis of imagery from satellites etc.

Histogram transformation is considered one of the fundamental processes for image enhancement of gray level images, which facilitates subsequent higher level operations such as detection and identification. Histogram equalization and contrast manipulations are well-known methods for enhancing the contrast of a given image but most of them tend to be heuristic based on deep expert knowledge for image processing. Hence these techniques require a large amount of analysis and computation because of complicated formulations. Histogram equalization provides maximum information contained in the image which indirectly modifies the image histogram.

Optimization plays an imperative role in robotics, artificial intelligence, operational research, and different connected fields. It is the process of trying to find the best possible solution to an optimization problem within a reasonable time limit. Several evolutionary algorithms such as Histogram Equalization (HE), Automatic Brightness and Contrast Optimization with Optional Histogram Clipping (ABCOOHC), Automatic Gain Control (AGC), Brightness Preserving Bi-Histogram Equalization (BBHE), CLAHE and Dualistic Sub-Image Histogram Equalization (DSIHE) have been introduced in recent years in the field of image processing because of their fast computing ability.


A top-selling method for image enhancement is histogram equalization (HE). This method is to enhance the image quality and relatively make better representation on different types of images. The operation of HE is acted by remapping the gray levels of the image established the probability distribution of the input gray levels. It flattens and stretches the dynamic range of the image’s histogram resulting in overall contrast enhancement.