Adaptive total variation denoising based on difference curvature

Chen, Qiang; Montesinos, Philippe; Sun, Quan Sen; Heng, Pheng‐Ann; Xia, De Shen

doi:10.1016/j.imavis.2009.04.012

Cited by 167 publications

(73 citation statements)

References 31 publications

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“…Bilateral filtering [19] PM model [16], complex diffusion filter [17], Ramp preserving Perona-Malik (RPPM) model [21], Rudin-Osher-Fatemi (ROF) model [22], and Adaptive total variation (ATV) model [20] Non local means (NL-means) method [23], HS based method [13], and block matching and 3D filtering (BM3D effective information related to retinal diseases are located between the retinal nerve fiber layer (RNFL, which inner boundary is marked with the dashed yellow curve in Fig. 2a) and the choroid region, we adopted different denoising methods for different regions in order to decrease the computing complexity.…”

Section: Representative Methodsmentioning

confidence: 99%

“…To make the homogeneity similarity-based method more suitable for retinal OCT images, two modifications are presented by considering the OCT image characteristics. To test the performance of the proposed method, we also provide a comparison both in terms of visual quality of the results and in quantitative measurements such as peak signal-to-noise ratio and mean structure similarity with other well-known denoising methods: the classic bilateral filtering [20], five PDE-based methods [17,18,[21][22][23], and three patch-based methods [14,24,25].…”

Section: Introductionmentioning

confidence: 99%

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Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images

et al. 2014

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Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging-a highresolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for OCT image removal, we improve it by considering the noise and retinal characteristics of OCT images in two respects: (1) median filtering preprocessing is used to make the noise distribution of OCT images more suitable for patchbased methods; (2) a rectangle neighborhood and region restriction are adopted to accommodate the horizontal stretching of retinal structures when observed in OCT images. As a performance measurement of the proposed technique, we tested the method on real and synthetic noisy retinal OCT images and compared the results with other well-known spatial denoising methods, including bilateral filtering, five partial differential equation (PDE)-based methods, and three patchbased methods. Our results indicate that our proposed method seems suitable for retinal OCT imaging denoising, and that, in general, patch-based methods can achieve better visual denoising results than point-based methods in this type of imaging, because the image patch can better represent the structured information in the images than a single pixel. However, the time complexity of the patch-based methods is substantially higher than that of the others.

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Section: Representative Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images

et al. 2014

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“…ujηη and ujεε are the secondorder directional derivatives of uj, as defined in Ref. [27]. Then, we define a spatially adaptive weighting matrix W, whose elements are defined as…”

Section: Spectral-spatial Adaptive Unidirectional Variation Modelmentioning

confidence: 99%

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Zhou

Wang

Huo

et al. 2016

Journal of the Optical Society of Korea

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Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l1-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

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“…The classical TV prior [23][24][25][26]31,36] can preserve the edges well, but aberrations occur when recovering smooth regions or weak edges; therefore, selective methods have been explored in recent years to obtain a solution to this shortcoming [37][38][39][40]. "Selective" means using different norms q (1 ≤ q ≤ 2) for different pixels.…”

Section: Adaptive-norm Prior For the Am Frameworkmentioning

confidence: 99%

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

et al. 2014

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Abstract:In this paper, a blind restoration method is presented to remove the blur in remote sensing images. An alternating minimization (AM) framework is employed to simultaneously recover the image and the point spread function (PSF), and an adaptive-norm prior is used to apply different constraints to smooth regions and edges. Moreover, with the use of the knife-edge features in remote sensing images, an automatic knife-edge detection method is used to obtain a good initial PSF for the AM framework. In addition, a no-reference (NR) sharpness index is used to stop the iterations of the AM framework automatically at the best visual quality. Results in both simulated and real data experiments indicate that the proposed AM-KEdge method, which combines the automatic knife-edge detection and the AM framework, is robust, converges quickly, and can stop automatically to obtain satisfactory results.

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Adaptive total variation denoising based on difference curvature

Cited by 167 publications

References 31 publications

Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images

Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

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