Deep residual refining based pseudo-multi-frame network for effective single image super-resolution

Single image super-resolution (SISR) has gained great attraction and progress in recent years. Since the SISR is an ill-posed inverse problem, most researchers are concentrated on making efforts to learn effective and reasonable mapping functions from low-resolution observation to its potential high-resolution (HR) counterpart. In this study, the authors have proposed a deep residual refining based pseudo-multi-frame network for efficient SISR. A channel-wise attention mechanism is employed for residual refinement. It can ease residual learning process through explicitly modelling non-linear dependencies between channels by using global information embedding. Multiple potential HRs from different deconvolutional layers are further artificially learned, and then adaptively fused into final desired HR image. The authors call this strategy as pseudo-multi-frame SR. It could make full use of available redundant information possessed in hierarchical layers. They have evaluated the proposed network on several popular benchmark datasets. The experimental results have shown that the two highlights proposed can consistently boost final performance. The proposed network can outperform most of the state-of-the-art methods with acceptable less parameters.