The exploration of underwater resources and the study of life have advanced over the years and are achieved using autonomous machines. The images taken during such missions are generally of low quality because of the dispersion of light by suspended particles and the depth of water bodies. Such problems have sparked innovations to correct the low-quality images to make them better to undergo research. This paper presents an innovative solution to correct the poor image quality disparity. In this paper, a combination of Relative Global Histogram Stretching (RGHS) and Contrast-Limited Adaptive Histogram Equalization (CLAHE) was used to correct the images. The idea proposes to take the input image and separate it into two parts: the RGB channels, which get corrected by CLAHE, and HSV, which get corrected by RGHS, by using it on the V channel. Then the corrected output from both RGHS and CLAHE is fused together using Euclidean Norm and Normalization to produce the desired output. The images are finally checked using various parameters like UIQM, UCIQE, entropy, etc. The proposed work was compared with many existing works like CLAHE, RGHS, IBLA, etc., and it achieved better results. The proposed work achieved an improvement of more than the average 10% of the existing work.