Super-resolution benchmarking {#tutorial_dnn_superres_benchmark}

Benchmarking

The super-resolution module contains sample codes for benchmarking, in order to compare different models and algorithms. Here is presented a sample code for performing benchmarking, and then a few benchmarking results are collected. It was performed on an Intel i7-9700K CPU on an Ubuntu 18.04.02 OS.

Source Code of the sample

@includelineno dnn_superres/samples/dnn_superres_benchmark_quality.cpp

Explanation

-# Read and downscale the image

@code{.cpp}
 int width = img.cols - (img.cols % scale);
 int height = img.rows - (img.rows % scale);
 Mat cropped = img(Rect(0, 0, width, height));
 Mat img_downscaled;
 cv::resize(cropped, img_downscaled, cv::Size(), 1.0 / scale, 1.0 / scale);
@endcode

Resize the image by the scaling factor. Before that a cropping is necessary, so the images will align.

-# Set the model

@code{.cpp}
DnnSuperResImpl sr;
sr.readModel(path);
sr.setModel(algorithm, scale);
sr.upsample(img_downscaled, img_new);
@endcode

Instantiate a dnn super-resolution object. Read and set the algorithm and scaling factor.

-# Perform benchmarking

@code{.cpp}
double psnr = PSNR(img_new, cropped);
Scalar q = cv::quality::QualitySSIM::compute(img_new, cropped, cv::noArray());
double ssim = mean(cv::Vec3f(q[0], q[1], q[2]))[0];
@endcode

Calculate PSNR and SSIM. Use OpenCVs PSNR (core opencv) and SSIM (contrib) functions to compare the images.
Repeat it with other upscaling algorithms, such as other DL models or interpolation methods (eg. bicubic, nearest neighbor).

Benchmarking results

Dataset benchmarking

###General100 dataset

#####2x scaling factor

	Avg inference time in sec (CPU)	Avg PSNR	Avg SSIM
ESPCN	0.008795	32.7059	0.9276
EDSR	5.923450	34.1300	0.9447
FSRCNN	0.021741	32.8886	0.9301
LapSRN	0.114812	32.2681	0.9248
Bicubic	0.000208	32.1638	0.9305
Nearest neighbor	0.000114	29.1665	0.9049
Lanczos	0.001094	32.4687	0.9327

#####3x scaling factor

	Avg inference time in sec (CPU)	Avg PSNR	Avg SSIM
ESPCN	0.005495	28.4229	0.8474
EDSR	2.455510	29.9828	0.8801
FSRCNN	0.008807	28.3068	0.8429
LapSRN	0.282575	26.7330	0.8862
Bicubic	0.000311	26.0635	0.8754
Nearest neighbor	0.000148	23.5628	0.8174
Lanczos	0.001012	25.9115	0.8706

#####4x scaling factor

	Avg inference time in sec (CPU)	Avg PSNR	Avg SSIM
ESPCN	0.004311	26.6870	0.7891
EDSR	1.607570	28.1552	0.8317
FSRCNN	0.005302	26.6088	0.7863
LapSRN	0.121229	26.7383	0.7896
Bicubic	0.000311	26.0635	0.8754
Nearest neighbor	0.000148	23.5628	0.8174
Lanczos	0.001012	25.9115	0.8706

Images

####2x scaling factor

Set5: butterfly.png	size: 256x256

PSRN / SSIM / Speed (CPU)	26.6645 / 0.9048 / 0.000201	23.6854 / 0.8698 / 0.000075	26.9476 / 0.9075 / 0.001039

29.0341 / 0.9354 / 0.004157	29.0077 / 0.9345 / 0.006325	27.8212 / 0.9230 / 0.037937	30.0347 / 0.9453 / 2.077280

####3x scaling factor

Urban100: img_001.png	size: 1024x644

PSRN / SSIM / Speed (CPU)	27.0474 / 0.8484 / 0.000391	26.0842 / 0.8353 / 0.000236	27.0704 / 0.8483 / 0.002234
		LapSRN is not trained for 3x because of its architecture
28.0118 / 0.8588 / 0.030748	28.0184 / 0.8597 / 0.094173		30.5671 / 0.9019 / 9.517580

####4x scaling factor

Set14: comic.png	size: 250x361

PSRN / SSIM / Speed (CPU)	19.6766 / 0.6413 / 0.000262	18.5106 / 0.5879 / 0.000085	19.4948 / 0.6317 / 0.001098

20.0417 / 0.6302 / 0.001894	20.0885 / 0.6384 / 0.002103	20.0676 / 0.6339 / 0.061640	20.5233 / 0.6901 / 0.665876