[IPOL announce] new article: Small Neural Networks can Denoise Image Textures Well: a Useful Complement to BM3D

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2016/150/

Yi-Qing Wang,
Small Neural Networks can Denoise Image Textures Well: a Useful 
Complement to BM3D,
Image Processing On Line, 6 (2016), pp. 1–7.
http://dx.doi.org/10.5201/ipol.2016.150

Abstract
Recent years have seen a surge of interest in deep neural networks 
fueled by their successful applications in numerous image processing and 
computer vision tasks. However, such applications typically come with 
huge computational loads. In this article, we explore the possibility of 
using small neural networks to denoise images. In particular, we present 
SSaNN (Self-Similarity and Neural Networks), a denoising algorithm which 
combines the strength of BM3D on large-scale structured patterns with 
that of neural networks on small-scale texture content. This algorithm 
is able to produce a better overall recovery than both BM3D and small 
neural networks.