Comparative Analysis of Image Enhancement Algorithms

Abstract

In the complex instruments utilised in essential fields such as satellite cameras, CT scanners, and High-Resolution Cameras (Underwater), image capture is critical without human-rated aberrations, sounds, or atmospheric disturbances. Even full reference QA (quality assessment) approaches have a limited ability to predict quality accurately. As a result, the difficulty of evaluating and enhancing photographs is further subdivided into domain-specific issues by focusing on a small set of artefacts. The most popular is entropy, which is usually relevant in picture coding: it is a lower limit for the average coding length in bits per pixel that may be attained without any loss of information by an optimal coding scheme. The word 'specific' is significant because it establishes right away that the strategies covered in this paper are primarily problemsolving techniques. For example, a procedure that works well for improving X-ray images may not be the ideal option. Thus, a method that works well for boosting X-ray photos may not be the greatest option for enhancing photographs obtained by a satellite thousands of miles away from the Earth. Image enhancement algorithms proposed in this paper are Intensity-Hue-Saturation transformation, Histogram Equalization algorithms, Edge Detection techniques and Retinex theory algorithms. These algorithms are implemented under satellite imagery, medical scans, underwater images, and their parameter analysis.