Blind deblurring and denoising via a learning deep CNN denoiser prior and an adaptive L ₀ ‐regularised gradient prior for passive millimetre‐wave images

Abstract

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.

Characterization of Flexible Amorphous Silicon Thin-Film Transistor-Based Detectors with Positive-Intrinsic-Negative Diode in Radiography

“…Therefore, noise amplification is inevitable. A deep learning-based deconvolution method that considers the noise level has been suggested as an alternative method to overcome this problem [ 41 , 42 ]. It is difficult to perform deconvolution when the PSF is unknown (blind deconvolution).…”

A Novel Passive Millimeter Wave Image Noise Suppression Method Based on Pixel Non-local Self-similarity

“…Initially, we apply Wiener soft threshold blind denoising on the noise coefficient matrix in the Haar transform domain. We construct the Wiener filter coefficients using the previously estimated signal intensity in (10) and noise intensity in (7), and perform Wiener filtering on C g l in (8):…”

“…Currently, there are two main research focuses on PMMW image denoising. One approach involves modifying the inversion algorithm for different imaging mechanisms [6][7][8][9][10], while the other aims to improve image quality based on image features [2,[11][12][13]. For instance, an adaptive reconstruction method has been proposed for the total-power radiometer imaging mechanism [8].…”

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

“…Among image restoration techniques, MRA has been representatively used because it can effectively separate several frequency components while considering the spatial domain [30,31]. Recently, convolution neural network denoiser and regularized term-based iterative method was presented to restore the noise and sharpness [32]. Moreover, deep learning module based on U-Net and half instance normalization block was introduced and this showed the outstanding results in terms of visual perception and evaluation metrics [33].…”