Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)

Chris M. Ward; Joshua Harguess; Brendan Crabb; Shibin Parameswaran

doi:10.1117/12.2275157

19 September 2017 Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)

Chris M. Ward, Joshua Harguess, Brendan Crabb, Shibin Parameswaran

Author Affiliations +

Proceedings Volume 10396, Applications of Digital Image Processing XL; 1039605 (2017) https://doi.org/10.1117/12.2275157
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to under- standing the capabilities and limitations of modern image processing methods - particularly those enabled by deep learning. When applying image processing in engineering solutions, a scientist or engineer has a need to justify their design decisions with clear metrics. By applying blind/referenceless image spatial quality (BRISQUE), Structural SIMilarity (SSIM) index scores, and Peak signal-to-noise ratio (PSNR) to images before and after im- age processing, we can quantify quality improvements in a meaningful way and determine the lowest recoverable image quality for a given method.

Citation Download Citation

Chris M. Ward, Joshua Harguess, Brendan Crabb, and Shibin Parameswaran "Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)", Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039605 (19 September 2017); https://doi.org/10.1117/12.2275157

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