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Showing 1 to 6 of 6 for “"Sparsifying Transforms"”.
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Tomographic reconstruction with adaptive sparsifying transforms
… reconstruction framework with an adaptive sparsifying transform penalty. An alternating minimization approach is used to jointly reconstruct the image while learning a sparsifying transform adapted to the particular image being reconstructed. The Alternating Direction Method of Multipliers …
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Adaptive sparse representations and their applications
… processing, and medical imaging. Analytical sparsifying transforms such as Wavelets and DCT have been widely used in compression standards. Recently, the data-driven learning of synthesis sparsifying dictionaries has become popular especially in applications such as denoising, inpainting, and …
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Transform learning based image and video processing
… from a good adaptive sparse model. Recently, the sparsifying transform model has received interest, for which sparse coding is cheap and exact, and learning, or data-driven adaptation admits computationally efficient solutions. In this thesis, we present two extensions to the transform learning …
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Magnetic resonance image reconstruction from highly undersampled K-Space data using dictionary learning
… data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this thesis, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly …
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Parsimonious models for inverse problems
Made available in DSpace on 2020-03-02T22:10:20Z (GMT). No. of bitstreams: 2 PFISTER-DISSERTATION-2019.pdf: 5777487 bytes, checksum: a5face6e9622e2c59c29e773b9e91482 (MD5) LICENSE.txt: 4209 bytes, checksum: 25546ae0ab408cc4a6ef95982f2166f7 (MD5) Previous issue date: 2019-08-20
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Adaptive nonlocal and structured sparse signal modeling and applications
… sparse coding and learning steps. Recently, sparsifying transform learning received interest for its cheap computation and its optimal updates in the alternating algorithms. Prior works on transform learning have certain limitations, including (1) limited model richness and structure for …