Abstract
With the 5G and the popularity of high-definition and ultrahigh-definition equipment, people have increasingly higher requirements for the resolution of images or videos. However, the transmission pressure on servers is also gradually increasing. Therefore, superresolution technology has attracted much attention in recent years. Simultaneously, with the further development of deep learning techniques, superresolution research is shifting from the calculation of traditional algorithms to the deep learning method, which exhibits a greatly superior final display. First, the traditional block-matching-3D (BM3D) algorithm is formed as the postprocessing module, which can avoid the uneven edge of GAN network recovery, make the picture appear more authentic, and improve the viewer's subjective feelings. Next, the adaptive-downsampling model (ADM) is utilized to train models for specific camera styles. The high-resolution (HR) data sequence is subsequently downsampled to a low-resolution (LR) data sequence, enabling the superresolution algorithm to utilize this training set. This method can obtain better results and improve overall performance by 0.1~0.3 dB.