Super Resolution Reconstruction via Multiple Frames Joint Learning
Peng Wang, Xiyuan Hu, Bo Xuan, Jiancheng Mu, Silong Peng · 2011
This paper presents a novel multi-frame joint learning approach for image super resolution via sparse representation. Based on the assumption that several low-resolution patches degraded from a same high-resolution patch under subpixel translation can preserve similar structures, we can use those similar low-resolution patches together to recover the sparse coefficients for the corresponding high-resolution patch, and the differences between them can help to supply more information.So, unlike the learning-based super resolution algorithm from single image which uses one patch in the learning process, we take into consideration some other well matched patches in 3D domain. Computer simulations demonstrate that, comparing with those single frame learning algorithms, our method will not only restore more details but also can effectively overcome the over learning and is more robust to noise.