Open AccessBlind deblurring and denoising via a learning deep CNN denoiser prior and an adaptive L 0 ‐regularised gradient prior for passive millimetre‐wave images
Author(s) -
Sun Dianjun,
Shi Yu,
Feng Yayuan
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.1193
Subject(s) - deblurring , noise reduction , computer science , artificial intelligence , image restoration , deconvolution , noise (video) , point spread function , convolutional neural network , gradient descent , computer vision , pattern recognition (psychology) , image (mathematics) , algorithm , artificial neural network , image processing
Passive millimetre‐wave (PMMW) imaging frequently suffers from blurring and low resolution due to the long wavelengths. In addition, the observed images are inevitably disturbed by noise. Traditional image deblurring methods are sensitive to image noise, even a small amount of which will greatly reduce the quality of the point spread function (PSF) estimation. In this paper, we propose a blind deblurring and denoising method via a learning deep denoising convolutional neural networks (DnCNN) denoiser prior and an adaptive ‐regularized gradient prior for passive millimetre‐wave images. First, a blind deblurring restoration model based on the DnCNN denoising prior constraint is established. Second, an adaptive ‐regularized gradient prior is incorporated into the model to estimate the latent clear image, and the PSF is estimated in the gradient domain. In a multi‐scale framework, alternate iterative denoising and deblurring are used to obtain the final PSF estimation and noise estimation. Ultimately, the final clear image is restored by non‐blind deconvolution. The experimental results show that the algorithm used in this paper not only has good detail recovery ability but is also more stable to different noise levels. The proposed method is superior to state‐of‐the‐art methods in terms of both subjective measure and visual quality.