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Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modeling, and medical imaging. It is nontrivial to extend an algorithm developed for flat images to the spherical ones. In this work, we focus on the challenging task of spherical image inpainting with a deep learning-based regularizer. Instead of a naive application of existing models for planar images, we employ a fast directional spherical Haar framelet transform and develop a novel optimization framework based on a sparsity assumption of the framelet transform. Furthermore, by employing progressive encoder-decoder architecture, a new and better-performed deep CNN denoiser is carefully designed and works as an implicit regularizer. Finally, we use a plug-and-play method to handle the proposed optimization model, which can be implemented efficiently by training the CNN denoiser prior. Numerical experiments are conducted and show that the proposed algorithms can greatly recover damaged spherical images and achieve the best performance over purely using a deep learning denoiser and a plug-and-play model. [ABSTRACT FROM AUTHOR]

Color image enhancement is one of the most important tasks in image processing. In this paper, we focus on the red and/or blue attenuation problem that arises in underwater image enhancement. We propose and develop a novel spatial color compensation model based on saturation-value total variation (SV-TV). In the proposed model, the compensation of the red and/or blue attenuation is achieved by adding a fraction of the green channel to the red and/or blue channel. The fraction is determined by a spatially varying function, which is controlled by the TV regularization. In order to avoid overcompensation and too many color artifacts, we make use of the SV-TV to regularize the objective compensation result. We numerically apply the proximal alternating linearized minimization to solve the proposed minimization problem, and we give the convergence analysis of the proposed algorithm. Numerical examples are presented to demonstrate that the performance of the proposed color compensation model is better than that of other testing methods in terms of visual quality and certain criteria, such as peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and CIEde2000 color difference. [ABSTRACT FROM AUTHOR]

The l1-l2 regularization is a popular nonconvex yet Lipschitz continuous metric, which has been widely used in signal and image processing. The theory for the l1-l2 minimization method shows that it has superior sparse recovery performance over the classical l1 minimization method. The motivation and major contribution of this paper is to provide a positive answer to the open problem posed in [T.-H. Ma, Y. Lou, and T.-Z. Huang, SIAM J. Imaging Sci., 10 (2017), pp. 1346-1380] about the sufficient conditions that can be sharpened for the l1-l2 minimization method. The novel technique used in our analysis of the l1-l2 minimization method is a crucial sparse representation adapted to the l1-l2 metric which is different from the other state-of-the-art works in the context of the l1-l2 minimization method. The new restricted isometry property (RIP) analysis is better than the existing RIP based conditions to guarantee the exact and stable recovery of signals. [ABSTRACT FROM AUTHOR]

In this paper, we study nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ ONTD on the image data sets from the real world applications including face recognition, image representation, and hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to propose a new convex optimization model for multiplicative noise and blur removal. The main idea is to rewrite a blur and multiplicative noise equation such that both the image variable and the noise variable are decoupled. The resulting objective function involves the total variation regularization term, the term of variance of the inverse of noise, the ℓ1-norm of the data-fitting term among the observed image, and noise and image variables. Such a convex minimization model can be solved efficiently by using many numerical methods in the literature. Numerical examples are presented to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model can handle blur and multiplicative noise (Gamma, Gaussian, or Rayleigh distribution) removal quite well. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to develop a histogram equalization algorithm for image contrast enhancement. Our idea is to propose a variational approach containing an energy functional to determine a local transformation such that the histogram can be redistributed locally, and the brightness of the transformed image can be preserved. In order to minimize the differences among the local transformation at the nearby pixel locations, the spatial regularization of the transformation is also incorporated into the functional for the equalization process. In the variational problem, we consider both H1-norm regularization and total variation regularization of the transformation in the model. The existence and uniqueness of the minimizers of the two proposed models are studied and analyzed. Experimental results are reported to show that the performance of the proposed models are competitive with the other compared methods for several testing images. [ABSTRACT FROM AUTHOR]

In the image processing community, there have recently been many restoration and reconstruction problems that can be reformulated into linearly constrained convex programming models whose objective functions have separable structures. These favorable reformulations have promoted impressive applications of the alternating direction method (ADM) in the field of image processing. At each iteration, the computation of ADM is dominated by solving two subproblems exactly. However, in many restoration and reconstruction applications, it is cither impossible or extremely expensive to obtain exact solutions of these ADM subproblems. This fact urges the development on inexact versions of ADM, which allow the generated ADM subproblems to be solved approximately subject to certain inexactness criteria. In this paper, we develop some truly implementable inexact ADMs whose inexactness criteria controlling the accuracy of the ADM subproblems are easily implementable. The convergence of the new inexact ADMs will be proved. Numerical results on several image processing problems will be given to illustrate the effectiveness of the proposed inexact ADMs. [ABSTRACT FROM AUTHOR]

We address the minimization of regularized convex cost functions which are customarily used for edge-preserving restoration and reconstruction of signals and images. In order to accelerate computation, the multiplicative and the additive half-quadratic reformulation of the original cost-function have been pioneered in Geman and Reynolds [IEEE Trans. Pattern Anal. Machine Intelligence, 14 (1992), pp. 367--383] and Geman and Yang IEEE Trans. Image Process., 4 (1995), pp. 932--946]. The alternate minimization of the resultant (augmented) cost-functions has a simple explicit form. The goal of this paper is to provide a systematic analysis of the convergence rate achieved by these methods. For the multiplicative and additive half-quadratic regularizations, we determine their upper bounds for their root-convergence factors. The bound for the multiplicative form is seen to be always smaller than the bound for the additive form. Experiments show that the number of iterations required for convergence for the multiplicative form is always less than that for the additive form. However, the computational cost of each iteration is much higher for the multiplicative form than for the additive form. The global assessment is that minimization using the additive form of half-quadratic regularization is faster than using the multiplicative form. When the additive form is applicable, it is hence recommended. Extensive experiments demonstrate that in our MATLAB implementation, both methods are substantially faster (in terms of computational times) than the standard MATLAB Optimization Toolbox routines used in our comparison study. [ABSTRACT FROM AUTHOR]

Many image processing applications require computing approximate solutions of very large, ill-conditioned linear systems. Physical assumptions of the imaging system usually dictate that the matrices in these linear systems have exploitable structure. The specific structure depends on (usually simplifying) assumptions of the physical model and other considerations such as boundary conditions. When reflexive (Neumann) boundary conditions are used, the coefficient matrix is a combination of Toeplitz and Hankel matrices. Kronecker products also occur, but this structure is not obvious from measured data. In this paper we discuss a scheme for computing a (possibly approximate) Kronecker product decomposition of structured matrices in image processing, which extends previous work by Kamm and Nagy [SIAM J. Matrix Anal. Appl., 22 (2000), pp. 155–172] to a wider class of image restoration problems. [ABSTRACT FROM AUTHOR]

Blur removal is an important problem in signal and image processing. The blurring matrices obtained by using the zero boundary condition (corresponding to assuming dark background outside the scene) are Toeplitz matrices for one-dimensional problems and block-Toeplitz­Toeplitz- block matrices for two-dimensional cases. They are computationally intensive to invert especially in the block case. If the periodic boundary condition is used, the matrices become (block) circulant and can be diagonalized by discrete Fourier transform matrices. In this paper, we consider the use of the Neumann boundary condition (corresponding to a reflection of the original scene at the boundary). The resulting matrices are (block) Toeplitz-plus-Hankel matrices. We show that for symmetric blurring functions, these blurring matrices can always be diagonalized by discrete cosine transform matrices. Thus the cost of inversion is significantly lower than that of using the zero or periodic boundary conditions. We also show that the use of the Neumann boundary condition provides an easy way of estimating the regularization parameter when the generalized cross-validation is used. When the blurring function is nonsymmetric, we show that the optimal cosine transform preconditioner of the blurring matrix is equal to the blurring matrix generated by the symmetric part of the blurring function. Numerical results are given to illustrate the efficiency of using the Neumann boundary condition. [ABSTRACT FROM AUTHOR]