Enhancing image denoising by controlling noise incursion in learned dictionaries

Enhancing image denoising by controlling noise incursion in learned dictionaries

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 p...

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Bibliographic Details
Main Authors: Anamitra Makur (EEE), Sahoo, Sujit Kumar
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2015
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:https://hdl.handle.net/10356/107084
http://hdl.handle.net/10220/25303
http://dx.doi.org/10.1109/LSP.2015.2388712
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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