Multiple-image super resolution using both reconstruction optimization and deep neural network
Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma · 2017
We present an efficient multi-image super resolution (MISR) method. Our solution consists of a L1-norm optimized reconstruction scheme for super resolution (SR), and a three-layer convolutional network for artifacts removal, in a concatenated fashion. Such a two-stage method achieves excellent performance, which outperforms the existing state-of-the-art SR methods in both subjective and objective measurements (e.g., 5 to 7 dB improvements on popular image database using PSNR metric).