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Convolutional neural network (CNN)-based methods have achieved great successfor single-image superresolution (SISR). However, most models attempt toimprove reconstruction accuracy while increasing the requirement of number ofmodel parameters. To tackle this problem, in this paper, we study reducing thenumber of parameters and computational cost of CNN-based SISR methods whilemaintaining the accuracy of super-resolution reconstruction performance. Tothis end, we introduce a novel network architecture for SISR, which strikes agood trade-off between reconstruction quality and low computational complexity.Specifically, we propose an iterative back-projection architecture usingsub-pixel convolution instead of deconvolution layers. We evaluate theperformance of computational and reconstruction accuracy for our proposed modelwith extensive quantitative and qualitative evaluations. Experimental resultsreveal that our proposed method uses fewer parameters and reduces thecomputational cost while maintaining reconstruction accuracy againststate-of-the-art SISR methods over well-known four SR benchmark datasets. Codeis available at"https://github.com/supratikbanerjee/SubPixel-BackProjection_SuperResolution".

Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution