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Google's RAISR implementation was trained on 10000 advertising banner images. Comparison of Results Results on Set 14 Images Comparison of Results on Set 5 Images Upscale Factor DependenciesĪll the dependent Python modules needed for the using our RAISR implementation can be installed using pip package manager and are the following:Īdditionally, a requirements.txt file is provided for installing all the dependencies by running the following command in a shell:

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The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code.

The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). The source code released here is the Jalali-Lab implementation of the RAISR algorithm written in Python 3.x by Sifeng He, under the guidance of Prof. With sufficient training data, consisting of low and high resolution image pairs, RAISR algorithm tries to learn a set of filters which can be applied to an input image that is not in the training set, to produce a higher resolution version of it. RAISR (Rapid and Accurate Image Super Resolution) is an image processing algorithm published by Google Research in 2016. Jalali-Lab Implementation of RAISR Algorithm Contents