A Gated Deep Model for Single Image Super-Resolution Reconstruction
Nahid Qaderi, Parvaneh Saeedi · 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) · 2022
Several deep learning-based models for single image super-resolution (SISR) have been presented in recent years. For different image content, however, the performance of each Super Resolution (SR) model varies. For example, one model may outperform others on images with high and intricate textures, while another may outperform others on images of structured scenes. We present a method that uses image content to select the most suitable model for SR reconstruction by taking advantage of each image's dominant content. The obtained results confirm that the proposed model delivers better results for scene-specific content.