SRSW Method for Denoising & Super-Resolution of Medical Images

  • Dalia M S KMEA College, Ernakulam, Kerala, India
  • Vidya Har MEA College, Ernalulam, Kerala, India
DOI: https://doi.org/10.53555/cse.v1i10.1321
Keywords: Super, Resolution, Non-negative, quadratic programming, blind deconvolution, non-negative, sparse linear representation

Abstract

High resolution images are needed for many purposes. Medical imaging is one of the important
applications of high resolution images. Conversion of a low resolution image to a high resolution image is
the main theme. This process is called super-resolution. Non-negative quadratic programming approach is
used for getting the non-negative sparse linear representation of the input patch over the low resolution
patches from the database. The database contains high resolution and low resolution patch pairs. Edges of
the super-resolved image can be enhanced by using the blind deconvolution technique, there by getting a
sharp view of the image for treatment and diagnosis

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Author Biographies

Dalia M S, KMEA College, Ernakulam, Kerala, India

Post Graduate Student

Computer Science and Engineering, KMEA College, Ernakulam, Kerala, India

Vidya Har, MEA College, Ernalulam, Kerala, India

Assistant Professor

IT Department, KMEA College, Ernalulam, Kerala, India

Published
2015-10-31
How to Cite
M S, D., & Har, V. (2015). SRSW Method for Denoising & Super-Resolution of Medical Images. IJRDO -Journal of Computer Science Engineering, 1(10), 01-06. https://doi.org/10.53555/cse.v1i10.1321
Issue
Vol. 1 No. 10 (2015): IJRDO - Journal of Computer Science Engineering
Section
Articles

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