A Novel Technique for Texture and Edge Preservation Using Bilateral Filter

Kundu, Raka

doi:10.1007/s40009-020-00921-3

Cited by 2 publications

(3 citation statements)

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“…We consider that this phenomenon is related to the weighting function of RTV, because is only based on distance between pixels and , ignoring the importance of image gray-scale values. We draw lessons from the thought of bilateral filtering [ 33 , 34 ] to utilize spatial proximity and pixel value similarity as the weighting function of WIV. Therefore, bilateral weighted relative total variation (BRTV) is proposed to simultaneously maintain edges and further reduce noise.…”

Section: The Proposed Reconstruction Algorithmmentioning

confidence: 99%

“…We consider this phenomenon is related to the weighting function of RTV, because the weight is only based on distance between pixels, ignoring the importance of image gray-scale values. We draw lessons from the thought of bilateral filtering [ 33 , 34 ] to propose the bilateral weighted relative total variation (BRTV). Spatial proximity and pixel value similarity are utilized as the weighting function of WIV.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by [ 33 , 34 ], we first propose BRTV to simultaneously maintain edges and further reduce noise for image restoration, then propose the BRTV-regularized projections onto convex set (POCS-BRTV) model for LDCT reconstruction. Being different from POCS-RTV, the weights of WIV in POCS-BRTV rely on both the closeness to vicinity in the domain and similarity to vicinity in the range, which could better characterize the distance and difference between two pixels.…”

Section: Introductionmentioning

confidence: 99%

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Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction

Zeng

Chen

et al. 2022

J Digit Imaging

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Low-dose computed tomography (LDCT) has been widely used for various clinic applications to reduce the X-ray dose absorbed by patients. However, LDCT is usually degraded by severe noise over the image space. The image quality of LDCT has attracted aroused attentions of scholars. In this study, we propose the bilateral weighted relative total variation (BRTV) used for image restoration to simultaneously maintain edges and further reduce noise, then propose the BRTV-regularized projections onto convex sets (POCS-BRTV) model for LDCT reconstruction. Referring to the spacial closeness and the similarity of gray value between two pixels in a local rectangle, POCS-BRTV can adaptively extract sharp edges and minor details during the iterative reconstruction process. Evaluation indexes and visual effects are used to measure the performances among different algorithms. Experimental results indicate that the proposed POCS-BRTV model can achieve superior image quality than the compared algorithms in terms of the structure and texture preservation.

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Section: The Proposed Reconstruction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction

Zeng

Chen

et al. 2022

J Digit Imaging

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Spectral CT reconstruction via low-rank representation and structure preserving regularization

He¹,

Zeng²,

Xu³

et al. 2023

Phys. Med. Biol.

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Objective: With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cutting into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel, which cause the image qualities to be severely degraded by noise and artifacts. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper. Approach: To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel. Main results: Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively. Significance: We outline a multi-channel reconstruction algorithm tailored for spectral CT. The qualitative and quantitative comparisons present a significant improvement of image quality, indicating its promising potential in spectral CT imaging.

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A Novel Technique for Texture and Edge Preservation Using Bilateral Filter

Cited by 2 publications

References 5 publications

Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction

Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction

Spectral CT reconstruction via low-rank representation and structure preserving regularization

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