A novel truncated nonconvex nonsmooth variational method for SAR image despeckling

Guo, Mingqiang; Han, Chengde; Wang, Weina; Su, Zhong; Lv, Ruina; Liu, Zheng

doi:10.1080/2150704x.2020.1846820

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…Real-world SAR data (OurSAR) are captured by sensors mounted on an airplane (3GHz, sliding spotlight mode, X band, resolution: 0.05 m × 0.05 m), and images are obtained by using the autofocus method in [35]- [37]. Besides, the AIRSAR image (taken over Flevoland in Netherlands, used in [22]) and TerraSAR image (X band, HH polarization, resolution: 5 m × 5 m, used in [18]) are also adopted to evaluate the performance.…”

Section: Resultsmentioning

confidence: 99%

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Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Zhang

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) images are inherently degraded by the speckle noise due to the coherent imaging, which may affect the performance of subsequent image analysis task. To address this problem, a weighted sparse representation-based method is proposed in this article for SAR image despeckling. The homomorphic transformation is first adopted to convert multiplicative noise into additive one. Second, similar patches are grouped together to learn the adaptive dictionaries and sparse coefficients based on nonlocal self-similarity constraint. Moreover, weighted regularizations are adopted for coefficients to boost the performance. Finally, despeckling images are obtained via exponential transformation. Experimental results on synthetic and real-world SAR images demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

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Section: Resultsmentioning

confidence: 99%

“…Besides, the distance is calculated through lookup tables to accelerate this algorithm. The variational model-based methods provide another available way to remove the speckle noise, such as in [17] and [18], in which despeckling problems are transformed to minimize some energy functions.…”

Section: Learning An Sar Image Despeckling Model Viamentioning

confidence: 99%

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Zhang

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…9 In Ref. 37, the authors suggest a unique truncated nonconvex nonsmooth model to reduce the speckle noise in SAR pictures. Regularization and I-divergence integrity terms are also included in the formula.…”

Section: Journal Of Electronic Imagingmentioning

confidence: 99%

Review on nontraditional perspectives of synthetic aperture radar image despeckling

2022

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Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.

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“…Its setting is indeed related to the properties of the image of interest: it can be fixed either empirically to enhance sparsity (p < 1, see [84]) or regarded as an information tailored to the image itself which should thus be estimated appropriately. The TV p regulariser has proved to be effective for the solution of several imaging problems ranging from labelling and segmentation [96] to blind deblurring [84,97] and synthetic aperture radar (SAR) image despeckling [66] and many more. Its performance strongly depends on the selected/estimated value of p, whose setting may be hard in case of very heterogeneous images composed, for instance, by both smooth and piece-wise constant regions.…”

Section: A Partial Remedy: Space-invariant Tv Generalisationsmentioning

confidence: 99%

On and beyond Total Variation regularisation in imaging: the role of space variance

Pragliola¹,

Calatroni²,

Lanza³

et al. 2021

Preprint

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Over the last 30 years a plethora of variational regularisation models for image reconstruction has been proposed and thoroughly inspected by the applied mathematics community. Among them, the pioneering prototype often taught and learned in basic courses in mathematical image processing is the celebrated Rudin-Osher-Fatemi (ROF) model [118] which relies on the minimisation of the edge-preserving Total Variation (TV) semi-norm as regularisation term. Despite its (often limiting) simplicity, this model is still very much employed in many applications and used as a benchmark for assessing the performance of modern learning-based image reconstruction approaches, thanks to its thorough analytical and numerical understanding. Among the many extensions to TV proposed over the years, a large class is based on the concept of space variance. Space-variant models can indeed overcome the intrinsic inability of TV to describe local features (strength, sharpness, directionality) by means of an adaptive mathematical modelling which accommodates local regularisation weighting, variable smoothness and anisotropy. Those ideas can further be cast in the flexible Bayesian framework of generalised Gaussian distributions and combined with maximum likelihood and hierarchical optimisation approaches for efficient hyper-parameter estimation. In this work, we review and connect the major contributions in the field of space-variant TV-type image reconstruction models, focusing, in particular, on their Bayesian interpretation which paves the way to new exciting and unexplored research directions.

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A novel truncated nonconvex nonsmooth variational method for SAR image despeckling

Cited by 15 publications

References 28 publications

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Review on nontraditional perspectives of synthetic aperture radar image despeckling

On and beyond Total Variation regularisation in imaging: the role of space variance

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