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|Title:||Enhancing image denoising by controlling noise incursion in learned dictionaries||Authors:||Anamitra Makur (EEE)
Sahoo, Sujit Kumar
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing||Issue Date:||2015||Source:||Sujit, K. S., & Anamitra Makur. (2015). Enhancing image denoising by controlling noise incursion in learned dictionaries. IEEE signal processing letters, 22(8), 1123-1126.||Series/Report no.:||IEEE signal processing letters||Abstract:||Existing image denoising frameworks via sparse representation using learned dictionaries have an weakness that the dictionary, trained from noisy image, suffers from noise incursion. This paper analyzes this noise incursion, explicitly derives the noise component in the dictionary update step, and provides a simple remedy for a desired signal to noise ratio. The remedy is shown to perform better both in objective and subjective measures for lesser computation, and complements the framework of image denoising.||URI:||https://hdl.handle.net/10356/107084
|DOI:||10.1109/LSP.2015.2388712||Rights:||© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2015.2388712].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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