Two-Step Approach for the Restoration of Images Corrupted by Multiplicative Noise

Huang, Yu-Mei; Lu, De-Yong; Zeng, Tieyong

doi:10.1137/120898693

Cited by 30 publications

(21 citation statements)

References 49 publications

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“…In our model, we exploited a much more effective image regularization term with larger clique and adjustable potential functions. In [28], the nonlocal filtering algorithm proposed in [49] is exploited to accomplish the first step for removing multiplicative noise. The nonlocal filtering algorithm [49] makes use of the same framework as PPBit, but investigates a different similarity measure in the presence of multiplicative noise.…”

Section: Methodsmentioning

confidence: 99%

“…The fourth category, i.e., variational methods [48,7,25,12,22,30,11,24,21,28], minimizes some appropriate energy functionals consisted of an image prior regularizer and a data fitting term. As a well-known regularizer, total variation (TV) has been widely used for the despeckling task [48,7,21,28]. For instance, in [21] a new variational model based on a hybrid data term and the widely used TV regularizer is proposed for restoring blurred images with multiplicative noise.…”

Section: Introductionmentioning

confidence: 99%

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Speckle Reduction with Trained Nonlinear Diffusion Filtering

Feng

Chen

2016

J Math Imaging Vis

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Speckle reduction is a prerequisite for many image processing tasks in synthetic aperture radar (SAR) images, as well as all coherent images. In recent years, predominant state-of-the-art approaches for despeckling are usually based on nonlocal methods which mainly concentrate on achieving utmost image restoration quality, with relatively low computational efficiency. Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality. To this end, we exploit a newly-developed trainable nonlinear reaction diffusion(TNRD) framework which has proven a simple and effective model for various image restoration problems. {In the original TNRD applications, the diffusion network is usually derived based on the direct gradient descent scheme. However, this approach will encounter some problem for the task of multiplicative noise reduction exploited in this study. To solve this problem, we employed a new architecture derived from the proximal gradient descent method.} {Taking into account the speckle noise statistics, the diffusion process for the despeckling task is derived. We then retrain all the model parameters in the presence of speckle noise. Finally, optimized nonlinear diffusion filtering models are obtained, which are specialized for despeckling with various noise levels. Experimental results substantiate that the trained filtering models provide comparable or even better results than state-of-the-art nonlocal approaches. Meanwhile, our proposed model merely contains convolution of linear filters with an image, which offers high level parallelism on GPUs. As a consequence, for images of size $512 \times 512$, our GPU implementation takes less than 0.1 seconds to produce state-of-the-art despeckling performance.}Comment: to appear in Journal of Mathematical Imaging and Vision. Demo codes are available from https://1drv.ms/u/s!ApXF85Oq1kvqgcscP8GqUvPE-dF7i

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Feng

Chen

2016

J Math Imaging Vis

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“…Here, the p -norm function has been proved effective for unknown and mixture type of noises, and been successfully used in image processing realms [27].…”

Section: X(φ)mentioning

confidence: 99%

Reducing spatially varying out-of-focus blur from natural image

Fang

Zeng³

2017

Inverse Problems &Amp; Imaging

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In this paper, we focus on the challenging problem of removing the spatially varying out-of-focus blur from a single natural image. We first propose an effective method to estimate the blur map by the total variation refinement on Hölder coefficient, then discuss the properties of the corresponding kernel matrix. A tight-frame based energy functional, whose minimizer is related to the optimal defocus result, is thus built. For tackling functional more efficiently, we describe the numerical procedure based on an accelerated primal-dual scheme. To verify the effectiveness of our method, we compare it with some state-of-the-art schemes using both synthesized and natural images. Experimental results demonstrate that the proposed method performs better than the compared methods.

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“…In the first phase, the method uses a noise detector to identify which pixels are corrupted, and in the second phase, it reconstructs only the noisy pixels based on an objective function with an ℓ 1 -data fidelity term and with TV as a regularization term. The two-phase model has also been studied for other applications, for instance in [8], the authors apply the two-phase method to restore blurred images with impulse and Gaussian noise; in [23], a two-phase method is used for recovering images corrupted by multiplicative noise; in [7] and [11], a twophase method is used to simultaneously deblur and denoise an image with impulse noise. Different from [12], in the second phase of [7] and [11] the authors reconstruct the image based on a modified ℓ 1 -TV model where only noise-free pixels are kept in the ℓ 1 -data fidelity term, due to no useful information contained in impulse noise.…”

Section: Introductionmentioning

confidence: 99%

Total Variation Based Parameter-Free Model for Impulse Noise Removal

Sciacchitano

Dong

Andersen

2017

Numer. Math. Theory Methods Appl.

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We propose a new two-phase method for reconstruction of blurred images corrupted by impulse noise. In the first phase, we use a noise detector to identify the pixels that are contaminated by noise, and then, in the second phase, we reconstruct the noisy pixels by solving an equality constrained total variation minimization problem that preserves the exact values of the noise-free pixels. For images that are only corrupted by impulse noise (i.e., not blurred) we apply the semismooth Newton's method to a reduced problem, and if the images are also blurred, we solve the equality constrained reconstruction problem using a first-order primal-dual algorithm. The proposed model improves the computational efficiency (in the denoising case) and has the advantage of being regularization parameter-free. Our numerical results suggest that the method is competitive in terms of its restoration capabilities with respect to the other two-phase methods.

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Two-Step Approach for the Restoration of Images Corrupted by Multiplicative Noise

Cited by 30 publications

References 49 publications

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Reducing spatially varying out-of-focus blur from natural image

Total Variation Based Parameter-Free Model for Impulse Noise Removal

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