Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

Abstract

Underwater images degraded due to low contrast and visibility issues. Therefore, it is important to enhance the images and videos taken in the underwater environment before processing. Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images. The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available, low resolution, and blurriness in underwater images caused by the normal camera. Various researchers have proposed different solutions to overcome these problems. Dark channel prior (DCP) is one of the most used techniques which produced a better Peak Signal to Noise Ratio (PSNR) value. However, DCP has some issues such as it tends to darken images, reduce contrast, and produce halo effects. The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization (CLAHE) and the Adaptive Color Correction Method. The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and some images were collected from the internet. The measure of entropy (MOE), Measure of Enhancement (EME), Mean Square Error (MSE), and PSNR opted as performance measures during experiments. The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

“…In addition, there have been many variants of the CLAHE and AHE that address the characteristics of low image contrast and blurry images. For example, Al-hajlah et al [9] used CLAHE and adaptive color correction methods to address the problem of blurred underwater images caused by limited available light, low resolution, and regular cameras. Ye et al [10] proposed a bi-histogram equalization algorithm with adaptive image correction to solve the problems of image brightness bias, image over-enhancement, and gray level merging that occur in traditional histogram equalization algorithms.…”