Data Acquisition and Preparation for Dual-Reference Deep Learning of Image Super-Resolution

The performance of deep learning based image super-resolution (SR) methods depend on how accurately the paired low and high resolution images for training characterize the sampling process of real cameras. Low and high resolution (LR  ∼  HR) image pairs synthesized by degradation models (e.g., bicubic downsampling) deviate from those in reality; thus the synthetically-trained DCNN SR models work disappointingly when being applied to real-world …