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Super-resolution benchmarking {#tutorial_dnn_superres_benchmark}
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===========================
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Benchmarking
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----
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The super-resolution module contains sample codes for benchmarking, in order to compare different models and algorithms.
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Here is presented a sample code for performing benchmarking, and then a few benchmarking results are collected.
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It was performed on an Intel i7-9700K CPU on an Ubuntu 18.04.02 OS.
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Source Code of the sample
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-----------
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@includelineno dnn_superres/samples/dnn_superres_benchmark_quality.cpp
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Explanation
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-----------
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-# **Read and downscale the image**
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@code{.cpp}
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int width = img.cols - (img.cols % scale);
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int height = img.rows - (img.rows % scale);
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Mat cropped = img(Rect(0, 0, width, height));
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Mat img_downscaled;
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cv::resize(cropped, img_downscaled, cv::Size(), 1.0 / scale, 1.0 / scale);
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@endcode
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Resize the image by the scaling factor. Before that a cropping is necessary, so the images will align.
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-# **Set the model**
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@code{.cpp}
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DnnSuperResImpl sr;
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sr.readModel(path);
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sr.setModel(algorithm, scale);
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sr.upsample(img_downscaled, img_new);
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@endcode
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Instantiate a dnn super-resolution object. Read and set the algorithm and scaling factor.
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-# **Perform benchmarking**
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@code{.cpp}
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double psnr = PSNR(img_new, cropped);
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Scalar q = cv::quality::QualitySSIM::compute(img_new, cropped, cv::noArray());
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double ssim = mean(cv::Vec3f(q[0], q[1], q[2]))[0];
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@endcode
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Calculate PSNR and SSIM. Use OpenCVs PSNR (core opencv) and SSIM (contrib) functions to compare the images.
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Repeat it with other upscaling algorithms, such as other DL models or interpolation methods (eg. bicubic, nearest neighbor).
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Benchmarking results
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-----------
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## General100 dataset
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### 2x scaling factor
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| | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
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| ------------- |:-------------------:| ---------:|--------:|
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| ESPCN | **0.008795** | 32.7059 | 0.9276 |
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| EDSR | 5.923450 | **34.1300** | **0.9447** |
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| FSRCNN | 0.021741 | 32.8886 | 0.9301 |
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| LapSRN | 0.114812 | 32.2681 | 0.9248 |
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| Bicubic | 0.000208 | 32.1638 | 0.9305 |
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| Nearest neighbor | 0.000114 | 29.1665 | 0.9049 |
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| Lanczos | 0.001094 | 32.4687 | 0.9327 |
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### 3x scaling factor
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| | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
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| ------------- |:-------------------:| ---------:|--------:|
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| ESPCN | **0.005495** | 28.4229 | 0.8474 |
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| EDSR | 2.455510 | **29.9828** | **0.8801** |
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| FSRCNN | 0.008807 | 28.3068 | 0.8429 |
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| LapSRN | 0.282575 |26.7330 |0.8862 |
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| Bicubic | 0.000311 |26.0635 |0.8754 |
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| Nearest neighbor | 0.000148 |23.5628 |0.8174 |
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| Lanczos | 0.001012 |25.9115 |0.8706 |
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### 4x scaling factor
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| | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
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| ------------- |:-------------------:| ---------:|--------:|
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| ESPCN | **0.004311** | 26.6870 | 0.7891 |
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| EDSR | 1.607570 | **28.1552** | **0.8317** |
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| FSRCNN | 0.005302 | 26.6088 | 0.7863 |
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| LapSRN | 0.121229 |26.7383 |0.7896 |
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| Bicubic | 0.000311 |26.0635 |0.8754 |
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| Nearest neighbor | 0.000148 |23.5628 |0.8174 |
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| Lanczos | 0.001012 |25.9115 |0.8706 |
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## Images
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### 2x scaling factor
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|Set5: butterfly.png | size: 256x256 | ||
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|:-------------:|:-------------------:|:-------------:|:----:|
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|PSRN / SSIM / Speed (CPU)|26.6645 / 0.9048 / 0.000201 |23.6854 / 0.8698 / **0.000075** | **26.9476** / **0.9075** / 0.001039|
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|  |  | 
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|29.0341 / 0.9354 / **0.004157**| 29.0077 / 0.9345 / 0.006325 | 27.8212 / 0.9230 / 0.037937 | **30.0347** / **0.9453** / 2.077280 |
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### 3x scaling factor
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|Urban100: img_001.png | size: 1024x644 | ||
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|:-------------:|:-------------------:|:-------------:|:----:|
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|PSRN / SSIM / Speed (CPU)| 27.0474 / **0.8484** / 0.000391 | 26.0842 / 0.8353 / **0.000236** | **27.0704** / 0.8483 / 0.002234|
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||  | LapSRN is not trained for 3x <br/> because of its architecture | 
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|28.0118 / 0.8588 / **0.030748**| 28.0184 / 0.8597 / 0.094173 | | **30.5671** / **0.9019** / 9.517580 |
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### 4x scaling factor
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|Set14: comic.png | size: 250x361 | ||
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|:-------------:|:-------------------:|:-------------:|:----:|
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|||| |
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|PSRN / SSIM / Speed (CPU)| **19.6766** / **0.6413** / 0.000262 |18.5106 / 0.5879 / **0.000085** | 19.4948 / 0.6317 / 0.001098|
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||  |  | 
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|20.0417 / 0.6302 / **0.001894**| 20.0885 / 0.6384 / 0.002103 | 20.0676 / 0.6339 / 0.061640 | **20.5233** / **0.6901** / 0.665876 |
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### 8x scaling factor
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|Div2K: 0006.png | size: 1356x2040 | |
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|:-------------:|:-------------------:|:-------------:|
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|PSRN / SSIM / Speed (CPU)| 26.3139 / **0.8033** / 0.001107| 23.8291 / 0.7340 / **0.000611** |
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||  | |
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|26.1565 / 0.7962 / 0.004782| **26.7046** / 0.7987 / 2.274290 | |
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