Under Water Image Enhancement by Colour Convolution with Total variation

Due to concern about the current state of the world’s oceans, several large-scale scientific projects have begun to investigate the condition of our oceans. These projects are making use of underwater video sequences to monitor marine species. The move to using underwater video monitoring introduces labour intensive manual processing techniques. This leads to the need for an automated system capable of processing the data at a much greater speed. This thesis investigated whether the development of suitable image processing techniques could be used for pre-processing underwater images which enhance the image. The main objective of this thesis reduce the noise ratio and increase the PSNR of the image. In underwater situations, clarity of images is degraded by light absorption and scattering. This causes one colour to dominate the image. In order to improve the perception of underwater images. Improve the colour noise by convolution method which reduces the colour blurriness and total variation (TV) improve the noise after blurriness we get the significance improvement in PSNR from existing method approximate multiple five increments of PSNR.