Multi-scale low-rank approximation method for image denoising

Ou, Yang; Zhang, Bo; Li, Bailin

doi:10.1007/s11042-022-12083-z

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…where M SE = ∥x − x∥ 2 2 , the structural similarity index (SSIM) (22) and edge preservation index (EPI) (23).…”

Section: Evaluation Metricmentioning

confidence: 99%

“…The rank minimization of the re-formated image matrix improves the denoising process as described in and [22]. The multi scale non-local self similarity approach is used for reducing the ringing artifacts produced in the segment based algorithms [23]. The segmented denoising is efficient in 1D as well as in 2D signals as shown in a practical application given in [24].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Sorting Measures on Similar Segment Grouping based Denoising Algorithms

Preethisree

Vivekanand

Mishra

et al. 2023

Preprint

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Denoising is a fundamental challenge in the field of digital image processing and computer vision. Many of the denoising algorithms operate on segments of images in which non-locally distributed similar segments are identified and grouped to form sub-images. The denoising algorithm is applied on these sub-images to recover the actual image. The basis of such denoising algorithm is the existence of non-local self similarity or redundancy of segments within the natural images. But choosing the similarity measures for segment segregation is challenging. An analysis of various sorting measures and its effect on the performance of the denoising algorithms is presented.

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“…where M SE = ∥x − x∥ 2 2 , the structural similarity index (SSIM) (22) and edge preservation index (EPI) (23).…”

Section: Evaluation Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%