A new model for performance evaluation of denoising algorithms based on image quality assessment

Authors:
Priyanka Srivastava;Prateek Gupta;Satyam Bhardwaj;Vikrant Bhateja
Affiliations:
Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, U. P, India;Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, U. P, India;Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, U. P, India;Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, U. P, India
Venue:
Proceedings of the CUBE International Information Technology Conference
Year:
2012

Citing 4
Cited 1

Bilateral Filtering for Gray and Color Images

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Non-Local Algorithm for Image Denoising

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing
An SVD-based grayscale image quality measure for local and global assessment

IEEE Transactions on Image Processing

An Efficient Cluster-Based Routing Protocol for WSNs Using Time Series Prediction-Based Data Reduction Scheme

International Journal of Measurement Technologies and Instrumentation Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Performance of the image denoising algorithms can be evaluated as a function of two main post-filtering parameters- the noise remained (in the image) known as 'residual noise' and the blurring (or other artifacts) introduced in the image during reconstruction known as 'collateral distortion'. This paper proposes a new model for performance evaluation of image denoising algorithms based on the parameters mentioned above. The proposed model evaluates the quality of the filtered image by taking into account the degree of noise cancellation, different distortions that can be introduced during reconstruction, along with the detail preservation. Simulations are performed on filtered images obtained after deteriorations due to impulse, Gaussian and speckle noises respectively. The obtained results using the proposed model correlates well with the different quality evaluation indices and hence efficiently evaluate the quality of the denoised image.