Multi-Channel Convolutional Neural Networks for Image Super-Resolution
Shinya Ohtani, Yu Kato, Nobutaka Kuroki, Tetsuya Hirose, Masahiro Numa · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2017
This paper proposes image super-resolution techniques with multi-channel convolutional neural networks. In the proposed method, output pixels are classified into K×K groups depending on their coordinates. Those groups are generated from separate channels of a convolutional neural network (CNN). Finally, they are synthesized into a K×K magnified image. This architecture can enlarge images directly without bicubic interpolation. Experimental results of 2×2, 3×3, and 4×4 magnifications have shown that the average PSNR for the proposed method is about 0.2dB higher than that for the conventional SRCNN.