Sparsity Augmented Restoration of Underwater Images using Compressive Sensing

Abstract

The study of underwater environments is a difficult task because both laws of reflection and refraction are influenced in the visualization of underwater medium. Other variables, such as aquatic flora and fauna, also have a major effect on the lighting conditions. While optical processing of such images is regarded, degradation can take place due to various environmental and capturing device defects. Also, due to over hitting of samples, the algorithm based on traditional Nyquist algorithm can prove computationally sensitive and produce lower accuracy. Compressive Sensing (CS) provides an alternative approach to overcome the over-hitting problem. The efficiency of underwater algorithms can be improved by incoherent sampling combined with sparsity. The proposed model is applied to the underwater blurred image and the algorithm is evaluated for restoration using directional gradient priors. The contribution of the proposed algorithm can be characterized as: a) Incoherent sampling reduces the problem of hitting (b) Enhancement of sparsity coefficients for restoration algorithms. The proposed contribution to the application of underwater imaging is mainly because there are less random samples in the compressive sensing that produce better sparsity prior to the initial stage, which makes the solution to the ill-posed problem highly convergent with high accuracy. Therefore, we propose a new Temporal Sparse Bayesian Learning (TSBL) using compressive sensing that leads to higher resolution image enhancement in underwater conditions.