Paper Info
Title
Efficient And Fast Real-World Noisy Image Denoising By Combining Pyramid Neural Network And Two-Pathway Unscented Kalman Filter
Abstract
Recently, image prior learning has emerged as an effective tool for image denoising, which exploits prior knowledge to obtain sparse coding models and utilize them to reconstruct the clean image from the noisy one. Albeit promising, these prior-learning based methods suffer from some limitations such as lack of adaptivity and failed attempts to improve performance and efficiency simultaneously. With the purpose of addressing these problems, in this paper, we propose a Pyramid Guided Filter Network (PGF-Net) integrated with pyramid-based neural network and Two-Pathway Unscented Kalman Filter (TP-UKF). The combination of pyramid network and TP-UKF is based on the consideration that the former enables our model to better exploit hierarchical and multi-scale features, while the latter can guide the network to produce an improved (a posteriori) estimation of the denoising results with fine-scale image details. Through synthesizing the respective advantages of pyramid network and TP-UKF, our proposed architecture, in stark contrast to prior learning methods, is able to decompose the image denoising task into a series of more manageable stages and adaptively eliminate the noise on real images in an efficient manner. We conduct extensive experiments and show that our PGF-Net achieves notable improvement on visual perceptual quality and higher computational efficiency compared to state-of-the-art methods.
Year
DOI
Venue
2020
10.1109/TIP.2020.2965294
IEEE TRANSACTIONS ON IMAGE PROCESSING
Keywords
DocType
Volume
Image prior, real-world noisy image denoising, pyramid network, two-pathway unscented Kalman filter
Journal
29
Issue
ISSN
Citations 
1
1057-7149
0
PageRank 
References 
Authors
0.34
14
4
Name
Order
Citations
PageRank
Ruijun Ma152.78
Haifeng Hu227060.38
Songlong Xing362.49
Zhengming Li45312.35