Cloud K-SVD for Image Denoising
نویسندگان
چکیده
Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce mutual to represent low-dimensional geometric structures in image data. We present novel application of the as we use it recover both noiseless noisy images from overlapping patches. implement node network Kubernetes using Docker containers facilitate K-SVD. Results show approximately remove quantifiable amounts noise benchmark gray-scaled without sacrificing accuracy recovery; achieve an SSIM index 0.88, 0.91 0.95 between clean recovered for levels ($\mu$ = 0, $\sigma^{2}$ 0.01, 0.005, 0.001), respectively, which similar SOTA field. evidently able learn across AWGN images. The be used specific any network.
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ژورنال
عنوان ژورنال: SN computer science
سال: 2022
ISSN: ['2661-8907', '2662-995X']
DOI: https://doi.org/10.1007/s42979-022-01042-y