Non-blind Image Restoration Based on Convolutional Neural Network

Blind image restoration processors based on convolutional neural network (CNN) are intensively researched because of their high performance. However, they are too sensitive to the perturbation of the degradation model. They easily fail to restore the image whose degradation model is slightly different from the trained degradation model. In this paper, we propose a non-blind CNN-based image restoration processor, aiming to be robust against a perturbation of the degradation model compared to the blind restoration processor. Experimental comparisons demonstrate that the proposed non-blind CNN-based image restoration processor can robustly restore images compared to existing blind CNN-based image restoration processors.Comment: Accepted by IEEE 7th Global Conference on Consumer Electronics, 201

Surface density of states and topological edge states in non-centrosymmetric superconductors

We study Andreev bound state (ABS) and surface density of state (SDOS) of non-centrosymmetric superconductor where spin-singlet $d$-wave pairing mixes with spin-triplet $p$ (or $f)$-wave one by spin-orbit coupling. For $d_{xy} + p$-wave pairing, ABS appears as a zero energy state. The present ABS is a Majorana edge mode preserving the time reversal symmetry. We calculate topological invariant number and discuss the relevance to a single Majorana edge mode. In the presence of the Majorana edge mode, the SDOS depends strongly on the direction of the Zeeman field.Comment: 10 pages, 15 figure