A Pre-processing Approach Using IBPDPNet for Single Image Super Resolution

The single image super-resolution (SISR) technique turns a low resolution (LR) image into a clear image. Many techniques, including edge-directed, dictionaries-based, and polynomial-based are offered for SISR. However certain blurring effects are formed when the LR image is converted to a higher resolution (HR) image. Recently, deep-learning methods based on convolutional neural networks (CNN) have emerged and are being used for super resolution. It is capable of restoring comprehensive information in HR images. However, these techniques are computationally costly and have significant memory needs, making them difficult to adapt to real-time settings. To overcome the aforementioned issue, iterative back projection-based dual path CNN (IBPDPNet) is utilized, with the LR image shallow and deep features extracted independently using the improved residual block and the recursive dilated convolution block. To reduce the blurring effects after upscaling, one lost detail information of the LR image in terms of back projecting error is added to the deep, and shallow features. To produce the essential features from the retrieved features channel-wise attention is applied followed by pixel shuffle for feature up-sampling. By implementing the aforesaid technique, the HR image retained with edge, texture, and geometric regularity is generated. The efficiency of the proposed strategy is better in terms of subjective and objective quality compared to the existing approach.