Subjectively correlated estimation of noise due to blurriness distortion based on auto‐regressive model using the Yule–Walker equations. Issue 10 (1st October 2018)

• Record Type: Journal Article
• Authors: Nakhaei, Arash Ashtari; Helfroush, Mohammad Sadegh; Danyali, Habibollah; Ghanbari, Mohammed
• Is Part Of: IET image processing. Volume 12:Issue 10(2018)
• Page Start: 1788
• Page End: 1796
• Publication Date: 2018-10-01
• Subjects: filtering theory -- correlation methods -- medical image processing -- regression analysis -- image restoration -- autoregressive processes -- feature extraction; estimated noise energy -- map called stem noise energy map -- blurriness image distortion -- image quality metrics -- AR model input -- signal‐dependent image distortion -- ACF estimation -- auto‐correlation function coefficients -- blurred image -- low‐energy version -- auto‐regressive modelling -- Yule–Walker equations -- auto‐regressive model -- blurriness distortion -- subjectively correlated estimation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0916
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, a block‐based estimation of noise due to blurriness distortion is proposed based on auto‐regressive (AR) modelling. In the proposed method; a de‐correlated, low‐energy version of the blurred image is auto regressively modelled. To this end, AR parameters are estimated using the Yule–Walker equations. As these equations include auto‐correlation function (ACF) coefficients, ACF estimation is also required. The Yule–Walker equations are solved making use of Durbin–Levinson algorithm. Finally, noise energy is mathematically defined and computed for each block. Since blurriness is a signal‐dependent image distortion, estimating and describing its characteristics via a noise like that of the AR model input, is significant. In fact, extracting features of such 'noise' can lead to the design and development of a new method of image quality metrics. Inspired by the 'stem cells' concept in medical science that is convertible to other cell types, the AR model input is called 'stem noise'. To visualise contribution of the 'Stem Noise' in the reconstruction of blurriness image distortion, a map called stem noise energy map is created. It is shown that the characteristics of the estimated noise energy are well correlated with the human subjective scores.