240 research outputs found
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
Multi-Frame Quality Enhancement for Compressed Video
The past few years have witnessed great success in applying deep learning to
enhance the quality of compressed image/video. The existing approaches mainly
focus on enhancing the quality of a single frame, ignoring the similarity
between consecutive frames. In this paper, we investigate that heavy quality
fluctuation exists across compressed video frames, and thus low quality frames
can be enhanced using the neighboring high quality frames, seen as Multi-Frame
Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach
for compressed video, as a first attempt in this direction. In our approach, we
firstly develop a Support Vector Machine (SVM) based detector to locate Peak
Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame
Convolutional Neural Network (MF-CNN) is designed to enhance the quality of
compressed video, in which the non-PQF and its nearest two PQFs are as the
input. The MF-CNN compensates motion between the non-PQF and PQFs through the
Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement
subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help
of its nearest PQFs. Finally, the experiments validate the effectiveness and
generality of our MFQE approach in advancing the state-of-the-art quality
enhancement of compressed video. The code of our MFQE approach is available at
https://github.com/ryangBUAA/MFQE.gitComment: to appear in CVPR 201
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