Analysis Equalization Images Contrast Enhancement and Performance Measurement

These days, image processing is crucial, particularly when it comes to enhancing brightness, contrast, and image quality. The goal of this research is to develop three distinct methods for manipulating images and evaluating them using histogram, entropy, and PSNR—two image-specific metrics. Frame Fusion produces excellent results in image contrast, brightness, and enhancement through the standards of PSNR, histogram, and entropy. In comparison to its competitors, the technology performed better in terms of high pixel uniformity in images, consistency efficiency, processing and execution speed, and contrast quality. The aforementioned findings lead us to the conclusion that exposure frame fusion technology is highly effective at figuring out how to improve the contrast and brightness of computer images. Three image processing techniques were used: exposure frame fusion, dynamic histogram equalization, and histogram equalization. A comparison of the techniques using quantitative and physical criteria revealed that histogram equalization outperformed dynamic contrast techniques in several areas, including image uniformity, contrast quality, efficiency, execution speed, and accuracy of results. It is advised to use exposure frame fusion in addition to histogram equalization since it is the brightest, clearest, and most like the original images.