4.7 A 91mW 90fps Super-Resolution Processor for Full HD Images

Super resolution is the process of reconstructing a high-resolution (HR) image from a low-resolution (LR) one. Super-resolution technology enables high-resolution video streaming, image zoom-in, and far object recognition. Fig. 4.7.1 shows such an application scenario. The details of the videos/images can be reconstructed and projected to a higher-resolution screen, thereby providing a better visual experience. A hardware accelerator is needed to speed up the super-resolution process to support real-time high-resolution video streaming. Conventionally, dictionary-based approaches, such as ANR/GR [1] and A+ [2], convert the LR image into the HR one from learned mapping functions. Neural network (NN)-based algorithms generate better-quality super-resolution images by extracting features from training [3]. However, the complexity of the dictionary-based and the NN-based algorithms is excessively high, making them unsuitable for high-speed applications [4]. A rapid and accurate image super resolution (RAISR) algorithm [4] is proposed to achieve comparable quality with a much faster processing speed when compared to the previous solutions. It employs pre-learned filters to enhance the image quality based on bicubic interpolation. A pre-learned filter (also known as kernel) is selected by a hash function to address the structure-related details.