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40 lines
1.4 KiB
40 lines
1.4 KiB
#include "test_precomp.hpp" |
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namespace cvtest |
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{ |
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TEST(xphoto_dctimagedenoising, regression) |
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{ |
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cv::String dir = cvtest::TS::ptr()->get_data_path() + "dct_image_denoising/"; |
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int nTests = 1; |
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float psnrThreshold[] = {0.5}; |
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int psize[] = {8}; |
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double sigma[] = {9.0}; |
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for (int i = 0; i < nTests; ++i) |
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{ |
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cv::String srcName = dir + cv::format( "sources/%02d.png", i + 1); |
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cv::Mat src = cv::imread( srcName, 1 ); |
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cv::String previousResultName = dir + cv::format( "results/%02d.png", i + 1 ); |
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cv::Mat previousResult = cv::imread( previousResultName, 1 ); |
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cv::Mat sqrError = ( src - previousResult ).mul( src - previousResult ); |
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cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( sqrError.total()*sqrError.channels() ); |
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double psnr = 10*log10(3*255*255/(mse[0] + mse[1] + mse[2])); |
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cv::Mat currentResult, fastNlMeansResult; |
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cv::dctDenoising(src, currentResult, sigma[i], psize[i]); |
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cv::Mat sqrError = ( currentResult - previousResult ) |
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.mul( currentResult - previousResult ); |
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cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( sqrError.total()*sqrError.channels() ); |
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double psnr = 10*log10(3*255*255/(mse[0] + mse[1] + mse[2])) - psnr; |
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EXPECT_GE( psnr, psnrThreshold[i] ); |
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} |
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} |
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} |