NDLI: Image restoration by blind-Wiener filter

Content Provider	IET Digital Library
Author	Yoo, Jae Chern Ahn, Chang Wook
Abstract	Wiener filter yields the minimum-mean-square error between the restored image and the original image. However, to obtain an optimal result, there must be accurate knowledge of the power spectra of the noise and the original image besides the degradation function. Otherwise, it will lead to an undesirable restored result. This study presents a so-called blind-Wiener filter that can restore the original image when we have no knowledge of the power spectra of both noise and original image. It uses the fact that averaging several consecutively measured images together will enhance signal-to-noise ratio (SNR). The number of images to be averaged to reduce noise to an acceptable level was concluded to be around ten. Ten independent random noises were added to a given corrupted image, resulting in ten images with different noises and then each of them was restored by Wiener filter to yield ten Wiener filtered images. Finally, the corrupted image was restored by taking an average over the ten Wiener filtered images. Experiments were conducted in a practical setting to demonstrate the effectiveness of the proposed method. The experimental results show that all the images in the test set were vastly improved and some images gave almost comparable performance to the traditional Wiener filter known as the best restoration method in terms of peak SNR.
Starting Page	815
Ending Page	823
Page Count	9
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 12, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0693
Journal	IET Image Processing
Publisher Date	2014-08-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blind-Wiener Filter Computer Vision And Image Processing Technique Degradation Function Filtering Method in Signal Processing Image Denoising Image Restoration Independent Random Noises Interpolation And Function Approximation Least Mean Squares Method Minimum-mean-square Error MMSE Noise Image Noise Reduction Numerical Analysis Optical, Image And Video Signal Processing Original Image Peak SNR Power Spectra Signal to Noise Ratio Wiener Filter
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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