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The recent advances in deep learning indicate significant progress in thefield of single image super-resolution. With the advent of these techniques,high-resolution image with high peak signal to noise ratio (PSNR) and excellentperceptual quality can be reconstructed. The major challenges associated withexisting deep convolutional neural networks are their computational complexityand time; the increasing depth of the networks, often result in high spacecomplexity. To alleviate these issues, we developed an innovative shallowresidual feature representative network (SRFRN) that uses a bicubicinterpolated low-resolution image as input and residual representative units(RFR) which include serially stacked residual non-linear convolutions.Furthermore, the reconstruction of the high-resolution image is done bycombining the output of the RFR units and the residual output from the bicubicinterpolated LR image. Finally, multiple experiments have been performed on thebenchmark datasets and the proposed model illustrates superior performance forhigher scales. Besides, this model also exhibits faster execution time comparedto all the existing approaches.

Time accelerated image super-resolution using shallow residual feature representative network