With the advent of smart devices that support 4K and 8K resolution, Single Image Super Resolution (SISR) has become an important computer vision problem. However, most super resolution deep networks are computationally very expensive. In this paper, we propose SESR, a new class of Super-Efficient Super Resolution networks that significantly improve image quality and reduce computational complexity. Detailed experiments across six benchmark datasets demonstrate that SESR achieves similar or better image quality than state-of-the-art models while requiring 2x to 330x fewer Multiply-Accumulate (MAC) operations. As a result, SESR can be used on constrained hardware to perform x2 (1080p to 4K) and x4 SISR (1080p to 8K). Towards this, we simulate hardware performance numbers for a commercial mobile Neural Processing Unit (NPU) for 1080p to 4K (x2) and 1080p to 8K (x4) SISR. Our results highlight the challenges faced by super resolution on AI accelerators and demonstrate that SESR is significantly faster than existing models. Overall, SESR establishes a new Pareto frontier on the quality (PSNR)-computation relationship for the super resolution task.

本文提出了一种基于线性过参数化的卷积神经网络的超高清图像重建模型，其在六个基准数据集上取得了类似或更好的图像质量，需要比现有模型少2倍至330倍的操作，适用于受限制的硬件并可用于实际移动设备上进行超高清图像重建。

用于超高效超分辨率的可折叠线性模块