Image denoising based on the adaptive weighted TV regularization

Pang, Zhi-Feng; Zhang, Huili; Luo, Shousheng; Zeng, Tieyong

doi:10.1016/j.sigpro.2019.107325

Cited by 42 publications

(21 citation statements)

References 50 publications

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“…By recalling Eq. ( 2), the recent paper [11] plugs Γ = Λ α (g)R −θ(g) ∇, φ = • 2 , and p ∈ (0, 1) where Λ α and θ are both functions of g. Except the preference of the outer norm, this definition differs from the EADTV in the sense that it better recognizes the local edge orientations due to the more complex OFE method that it employs, and uses an adaptive stretching matrix weighted according to the first-order local edge information.…”

Section: Directional Total Variation (Dtv)mentioning

confidence: 99%

“…However, the noise heavily affects the edges and mostly the wrong directions are penalized. A very recent work [11] proposed an extended DTV model that follows a similar twostage approach. Distinctively, to extract directions, they employed a more sophisticated orientation field estimation (OFE) method used for fingerprint identification in [12].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive direction-guided structure tensor total variation

Demircan-Tureyen

Kamasak

2021

Signal Processing: Image Communication

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Section: Directional Total Variation (Dtv)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive direction-guided structure tensor total variation

Demircan-Tureyen

Kamasak

2021

Signal Processing: Image Communication

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“…This choice, however, is very sensitive to noise oscillations and it may misguide the local directional behaviour if these are too large. Alternatively, as considered in [65,58,54,93] and more recently in [109,50], the dependence on the image to retrieve can be encoded explicitly in the definition of the regularisation by allowing θ i to be a function of the target image u (i.e. θ i = θ i (u)) using, for instance, information coming from the structure tensor.…”

Section: Geometrical Interpretationmentioning

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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“…By choosing the regularization parameter based on the inverse gradient, in [31], anh et al proposed some adaptive image restoration methods. Besides, in [32,33], the authors proposed the adaptive TV p regularization and the adaptive weighted TV p regularization for image denoising, respectively. In [34], a novel adaptive image denoising method was proposed, which combines a Tchebichef moment-based sparse regularizer with an adaptive steerable total variation regularizer.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Image Restoration via a Relaxed Regularization of Mean Curvature

Zhang

Deng

et al. 2020

Mathematical Problems in Engineering

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In this paper, a new relaxation model based on mean curvature for adaptive image restoration is proposed. To solve the problem efficiently, an alternating direction method of multipliers (ADMMs) is proposed. Furthermore, a rigorous convergence theory of the proposed algorithm is established. We also give the complexity analysis of our proposed method. Experimental results are provided to demonstrate the effectiveness and efficiency of the proposed method over a state-of-the-art method on synthetic and natural images.

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Image denoising based on the adaptive weighted TV regularization

Cited by 42 publications

References 50 publications

Adaptive direction-guided structure tensor total variation

Adaptive direction-guided structure tensor total variation

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

Adaptive Image Restoration via a Relaxed Regularization of Mean Curvature

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