LAUSR

Recent developments of image super-resolution often utilize the deep convolutional neural network (CNN) and residual learning to relate the observed low-resolution pixels and unknown high-resolution pixels. However, image interpolation assumes that the observed image was directly down-sampled without low-pass filtering, such that the aliased down-sampled low-resolution image exhibits jags and chaos that cannot be easily modeled by conventional residual learning in super-resolution. In this paper, we propose a new framework to exploit the residual dense network using hierarchical levels of recursive residual learning and densely connected convolutional layers for image interpolation. The proposed deep recursive network iteratively reconstructs hierarchical levels of image details for aliased and discontinuous residual of interpolated pixels. Experimental results on popular Set16, Set18, and Urban12 image datasets show that the proposed method outperforms state-of-the-art image interpolation methods using local and nonlocal autoregressive models, random forests and deep CNN, in terms of PSNR (0.27–1.57 dB gain), SSIM and subjective evaluations. More importantly, model parameters of the proposed method are significantly less than that of existing deep CNN for image interpolation.