Visual Quality Improvement In Deep Sea Images

Abstract

In image processing, image enhancement plays a significant role in improving image quality. This is done by emphasizing relevant information and suppressing irrelevant information. Due to the various impacts of the underwater medium, the image caught in the water could be more precise. These effects are controlled by the suspended particles that cause light to be absorbed and scattered as a picture is formed. The underwater environment brought low contrast and faded color problems, which could be more conducive to imaging data. As a result, the imaging data must be improved before moving on to further processing during any image-based exploration and inspection activities. This work proposes a fusion using wavelet technique to improve gloomy underwater visuals by fixing poor colour variance and colour manipulation issues. Various wavelet families, including Daubechies, Symlet, biorthogonal, Han Real, Beylkin, and Coiflet, are used in this study to fuse the images. Results show that Biorthogonal and Daubechies wavelet families perform significantly better than others. In recent years, Wavelet Transform (WT) has emerged as a powerful tool for time-frequency analysis, denoising, and signal coding.