Open AccessRole of approximation wavelet coefficients in blind image deconvolution
Author(s) -
Gao Qiong,
Qu Weidong,
Zhang Yanxiu,
Ma Na,
Lei Ping
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.3445
Subject(s) - deconvolution , wavelet , blind deconvolution , mathematics , image (mathematics) , wavelet transform , algorithm , wiener deconvolution , stationary wavelet transform , artificial intelligence , wavelet packet decomposition , computer science
The role of approximation coefficients (ACs) in blind image deconvolution (BID) is examined by restoring each sub‐band image individually in the wavelet domain, and excellent performance is achieved by an average strategy for the estimation of AC. The most important observation is that accurate deconvolution of AC is not necessary and some form of hybrid can reach a balance between sharpness and contrast. This finding leads to reappraising the common treatment of wavelet coefficients in deconvolution, which takes the coefficients in every sub‐band as a unity. It is argued that the AC of the blurred image can be regarded as a good approximation of the ‘true’ image with lower resolution. Wavelet transform of one level is preferred in the implementation and the difference between the method and common multiscale approach is stressed. With specific treatment of AC, the proposed method can be combined with many other BID algorithms to improve deconvolution performance.