A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

Abstract

This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.

A Codec-Unified Deblurring Approach Based on U-Shaped Invertible Network with Sparse Salient Representation in Latent Space

“…After finishing the training of all the invertible networks, we only need to input the blurry images to obtain the final deblurring results in an end-to-end manner. The blurry image x(0) is firstly transformed by Equation (5)a and Equation (6)a to the latent variable z(l) on each level l, and then mapped to the estimated sharp variable ẑ(l) by Equation (7). Finally, the x(0) is obtained by Equation ( 8) and Equation ( 9) as the reconstruction procedure.…”

“…However, image deblurring is a highly ill-posed problem because there are infinitely feasible solutions. In order to constrain the solution space to valid images, early deblurring methods typically use empirical observations to handcraft image priors to improve image quality [3][4][5][6][7]. In recent years, with the successful application of deep learning [2,[8][9][10], the deblurring methods based on convolutional neural networks (CNNs) that implicitly learn more general priors by capturing the statistical information of natural images from large-scale data have developed rapidly [11][12][13][14][15][16].…”

Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser

“…Non-blind restoration algorithms have made considerable progress in the field of image restoration. Several representative non-blind restoration algorithms have achieved ideal image restoration effects in their respective application fields, such as the Wiener filtering algorithm [ 34 ], total variation regularized image restoration algorithm [ 35 , 36 , 37 ], hybrid spatio-spectral total variation image restoration algorithm [ 38 ],image restoration algorithm based on the natural image gradient distribution model [ 39 ], and fast non-blind restoration algorithm based on natural image gradient constraints [ 40 ]. The authors in [ 41 ] proposed a local piecewise regularization Richardson Lucy (RL) method and constructed a new regularization term that effectively controlled the noise and edge ringing effect in the restored image.…”

Signal Processing and Analysis of Electrical Circuit

“…It was based on VMD (variational mode decomposition), DCO (duffing chaotic oscillator) and KPE (kind of permutation entropy) [2]. The next paper [3] presented two models (HOCTVL1 model and SAHOCTVL1 model) for solving the problem of image deblurring under impulse noise. The proposed models are good for recovering the corrupted images [3].…”

“…The next paper [3] presented two models (HOCTVL1 model and SAHOCTVL1 model) for solving the problem of image deblurring under impulse noise. The proposed models are good for recovering the corrupted images [3].…”