Underwater turbidity removal through ill-posed optimization with sparse modeling

In this paper, we incorporate an underwater deblurring method for the minimization of an energy function. The incorporated method uses ill-posed and sparse modeling for deblurring. The blur modeling is done for typical underwater turbidity which can be restored through the proposed algorithm. The incorporated algorithm uses intelligent adaptivity through the uses of trained dictionaries from local as well as global features. The result analysis showed that by achieving better image quality so far as for the performance metrics and also the performance of visual quality of the estimated image.