Numerical Simulation and Design of Hybrid Underwater Image Restoration and Enhancement with Deep Learning - Gupta, Yogesh Kumar; Saxena, Khushboo (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Academicians from all over the world have been researching underwater images and the ability to capture crystal clear images for the past few years. Additionally, restoring the acquired images requires a laborious process in its entirety. The obtained underwater images have some flaws because of the scientific phenomena of absorption and scattering. These images suffer from colour distortion, blurriness, and low contrast effects, which are the main issues. For researchers in the field of image processing, overcoming these deficiencies is a herculean task. When light passes through water, its path is constrained. Pictures become submerged and turn greenish blue as they fall short on certain frequency parts in this case because the larger frequencies are influenced more than the more restricted ones. For instance, a picture taken at a depth of about 4-5 m underwater would require red frequency because the more extended frequency ranges of the apparent range are weaker first. Other frequency segments will start to lose significance with further increase. As a result, the pictures suffer from the negative effects of limited perceivability range, uneven lighting, and the presence of splendid antiquities. The proposed research work uses a deep learning model to improve the underwater images to get around this.