IRGUN : Improved Residue Based Gradual Up-Scaling Network for Single Image Super Resolution

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Abstract

Convolutional neural network based architectures have achieved decent perceptual quality super resolution on natural images for small scaling factors (2X and 4X). However, image super-resolution for large magnication factors (8X) is an extremely challenging problem for the computer vision community. In this paper, we propose a novel Improved Residual based Gradual Up-Scaling Network (IRGUN) to improve the quality of the super-resolved image for a large magnification factor. IRGUN has a Gradual Upsampling and Residue-based Enhancment Network (GUREN) which comprises of series of Up-scaling and Enhancement blocks (UEB) connected end-to-end and fine-tuned together to give a gradual magnification and enhancement. Due to the perceptual importance of the luminance in super-resolution, the model is trained on luminance (Y) channel of the YCbCr image. Whereas, the chrominance components (Cb and Cr) channel are up-scaled using bicubic interpolation and combined with super-resolved Y channel of the image, which is then converted to RGB. A cascaded 3D-RED architecture trained on RGB images is utilized to incorporate its inter-channel correlation. In addition to this, the training methodology is also presented in the paper. In the training procedure, the weights of the previous UEB are used in the next immediate UEB for faster and better convergence. Each UEB is trained on its respective scale by taking the output image of the previous UEB as input and corresponding HR image of the same scale as ground truth to the successive UEB. All the UEBs are then connected end-to-end and fine tuned. The IRGUN recovers fine details effectively at large (8X) magnification factors. The efficiency of IRGUN is presented on various benchmark datasets and at different magnification scales.

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