Swift single image super resolution using deep convolution neural network
A. Dalvadi Jigar · 2016
Now a days, Image Super resolution is one of the most important and challenging issue in the image processing area. Aim of Super resolution is to generate high-resolution image from single or multiple low resolution of the same scene or image. With single low resolution image it's very challenging to produce high-resolution image because a single low-resolution image contain the less information. Due to the ability of preserving edges, kind of method called TV (Total Variation)-based method was proposed as regularization function for some inverse problems. Due to ill-posed nature of problem, existing super resolution method which based on combine total variation regularization term including the Non Local Total Variation (NLTV) and Steering Kernel Regularization Total Variation (SKRTV) which takes the more execution time due to the non-local weight calculation. We propose the Example based Convolution neural network which consists of three layers namely convolution layer, max-pooling layer and reconstruction layer. Using Convolution neural network approach we achieved to reduce the execution time as well as increased the PSNR ratio.