A Multi-scale Progressive Method of Image Super-Resolution

In recent year, researchers have gradually focused on single image super-resolution for large scale factors. Single image contains scarce high-frequency details, which is insufficient to reconstruct high-resolution image. To address this problem, we propose a multi-scale progressive image super-resolution reconstruction network (MSPN) based on the asymmetric Laplacian pyramid structure. Our proposed network allows us to separate the difficult problem into several subproblems for better performance. Specially, we propose an improved multi-scale feature extraction block (MSFB) to widen our proposed network and achieve deeper and more effective feature information exploitation. Moreover, weight normalization is applied into MSFB to tackle the gradient vanishing and gradient exploding problem, and to accelerate the convergence speed of training. In addition, we introduce pyramid pooling layer into the upsampling module to further enhance the image reconstruction performance by aggregating local and global context information. Extensive evaluations on benchmark datasets show that our proposed algorithm gains great performance against the state-of-the-art methods in terms of accuracy and visual effect.