Multilevel progressive recursive dilated networks with correlation filter (MPRDNCF) for image super-resolution

Nowadays, deep convolutional neural networks (CNNs) are mostly applied for image Super-Resolution (SR). But still, there are some disadvantages of using CNN as it causes enormous computational complexity as if it is directly applied to SR applications. In this paper, dilated convolution is adopted that expands receptive field without any pixel information losses. The dilated convolution is designed as recursive residual network; therefore, internal parameters are preserved. Therefore, the model is termed as Multilevel Progressive Recursive Dilated Networks with Correlation Filter (MPRDNCF) and adopted progressive approach with different levels of recursive dilated residual network that is interleaved with correlation filter for upscaling of image. This module upscales with different scaling factors and magnifies it using deconvolution layer. MPRDNCF model used progressive recursive dilated residual learning approach which shares the information between the convolution layers for the identity prior during the network training. The architecture of MPRDNCF is 33 layers of CNN. We have presented an ablation study on Set5, Set14, Urban100, and BSD100 datasets, and also presented its superior result with comparison to the existing technique of state-of-art.