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version (2.84 KB) by kiran kumar
Determine thresholds to minimize MSE,


Updated 18 Oct 2013

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Real world signals usually contain departures from the ideal signal that would be produced by our model of the signal production process. Such departures are referred to as noise. Noise arises as a result of unmodelled or unmoddellable processes going on in the production and capture of the real signal. It is not part of the ideal signal and may be caused by a wide range of sources, e.g variations in the detector sensitivity, environmental variations, the discrete nature of radiation, transmission or quantization errors, etc. it is also possible to treat irrelevant scene details as if they are image noise (e.g surface reflectance textures). The characteristics of noise depend on its source, as does the operator which best reduces its effects.

Many image processing packages contain operator to artificially add noise to an image. Deliberately corrupting an image with noise allows us to test the resistance of an image processing operator to noise and assess the performance of various noise filters.
This project implements image denoising using visushrink by using both soft and hard threshold methods..hope u like it..

Cite As

kiran kumar (2021). IMAGE DENOISING USING VISUSHRINK (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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