Abstract:Denoising images subjected to Gaussian and Poisson noise has attracted attention in many areas of image processing. This paper introduces an image denoising framework using higher order fractional overlapping group sparsity prior to sparser image representation constraint. The proposed prior has a capability of avoiding staircase effects in both edges and oscillatory patterns (textures). We adopt the alternating direction method of multipliers for optimizing the proposed objective function by converting it int… Show more
“…fractional order compensators [18], [19], fractional order observer [20] and fractional order sliding mode observer [21]. Inherent strengths of fractional calculus in terms of long-term memory, nonlocality and weak singularity makes it preferable to be applied in optimization problems such as signal and image processing [22], [23] and complex neural network training [24].…”
This paper investigates speed regulation of permanent magnet synchronous motor (PMSM) system based on sliding mode control (SMC). Sliding mode control has been vastly applied for speed control of PMSM. However, continuous SMC enhancement studies are executed to improve the performance of conventional SMC in terms of tracking and disturbance rejection properties as well as to reduce chattering effects. By introducing fractional calculus in the sliding mode manifold, a novel fractional order sliding mode controller is proposed for the speed loop. The proposed fractional order sliding mode speed controller is designed with a sliding surface that consists of both fractional differentiation and integration. Stability of the proposed controller is proved using Lyapunov stability theorem. The simulation and experimental results show the superiorities of the proposed method in terms of faster convergence, better tracking precision and better anti-disturbance rejection properties. In addition, chattering effect of this enhanced SMC is smaller compared to those of conventional SMC. Last but not least, a comprehensive comparison table summarizes key performance indexes of the proposed controller with respect to conventional integer order controller.
“…fractional order compensators [18], [19], fractional order observer [20] and fractional order sliding mode observer [21]. Inherent strengths of fractional calculus in terms of long-term memory, nonlocality and weak singularity makes it preferable to be applied in optimization problems such as signal and image processing [22], [23] and complex neural network training [24].…”
This paper investigates speed regulation of permanent magnet synchronous motor (PMSM) system based on sliding mode control (SMC). Sliding mode control has been vastly applied for speed control of PMSM. However, continuous SMC enhancement studies are executed to improve the performance of conventional SMC in terms of tracking and disturbance rejection properties as well as to reduce chattering effects. By introducing fractional calculus in the sliding mode manifold, a novel fractional order sliding mode controller is proposed for the speed loop. The proposed fractional order sliding mode speed controller is designed with a sliding surface that consists of both fractional differentiation and integration. Stability of the proposed controller is proved using Lyapunov stability theorem. The simulation and experimental results show the superiorities of the proposed method in terms of faster convergence, better tracking precision and better anti-disturbance rejection properties. In addition, chattering effect of this enhanced SMC is smaller compared to those of conventional SMC. Last but not least, a comprehensive comparison table summarizes key performance indexes of the proposed controller with respect to conventional integer order controller.
“…Adam et al [20] combined non-convex higher order TV with overlapping group sparsity to construct a hybrid model (HNHOTV), so that it can maintain the uniformity of the staircase edge. Kumar et al [21] proposed a model of combing higher order fractional TV and overlapping group sparsity to retain the texture pattern in the image while decrease staircase artifacts. In our previous work [22], we combined high-order total variation with overlapping group sparsity (OGSHOTV).…”
In this paper, a new model combining four-directional total variation with overlapping group sparsity is proposed, which not only suppresses the staircase effects introduced by traditional total variation, but also fully utilizes the gradient neighborhood information on each pixel of the image. In order to decrease the computation time of image denoising, the alternating direction method of multipliers (ADMM) is adopted to divide the complex optimization problem into separate subproblems that are easy to solve. At the same time, two-dimensional Fast Fourier Transform (FFT) and majorization-minimization (MM) are used to solve the subproblems alternatively. Then, the proposed new model is compared with other state-of-the-art models. Experiments show that the new model is robust in denoising. The new model not only excavates the gradient information of the four directions on the image to remove the noise more effectively, but also better in preserving image features, further reducing staircase artifacts. INDEX TERMS Image denoising; Four-directional total variation; Overlapping group sparsity; ADMM.
“…As another degradation factor, image formation may involve undesirable blurrings such as motion or out-of-focus. By rewriting the concerned images into column-major vectorized form, we can regard the observed image g 2 R n�n as a realization of Poisson random vector with expected value Hf + b, where H is a n 2 × n 2 convolution matrix corresponding to the point spreading function (PSF) which models blur effects, f 2 R n�n is the original image, and b 2 R n�n is a nonnegative constant background [4][5][6].…”
The restoration of the Poisson noisy images is an essential task in many imaging applications due to the uncertainty of the number of discrete particles incident on the image sensor. In this paper, we consider utilizing a hybrid regularizer for Poisson noisy image restoration. The proposed regularizer, which combines the overlapping group sparse (OGS) total variation with the high-order nonconvex total variation, can alleviate the staircase artifacts while preserving the original sharp edges. We use the framework of the alternating direction method of multipliers to design an efficient minimization algorithm for the proposed model. Since the objective function is the sum of the non-quadratic log-likelihood and nonconvex nondifferentiable regularizer, we propose to solve the intractable subproblems by the majorization-minimization (MM) method and the iteratively reweighted least squares (IRLS) algorithm, respectively. Numerical experiments show the efficiency of the proposed method for Poissonian image restoration including denoising and deblurring.
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