Multi-scale Adaptive Residual Network Using Total Variation for Real Image Super-Resolution

Single image super-resolution (SISR) has developed fast for recent years. Most of the SISR models are trained and evaluated with simulated data where low-resolution (LR) images are generated from high-resolution (HR) images using pre-defined degradation. In contrast, real-world image super-resolution (RealSR) is more challenging since the process of obtaining LR images is formulated by complex degradation. To solve this problem, we propose the multi-scale adaptive real image super-resolution (MARS). Our model extracts complex features in the image and uses them for upscaling adaptively. Experimental results show that the proposed method can improve the quality of the super-resolved images in RealSR.