You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Merge pull request #2231 from Saafke:dnn_superres_final_phase
* Add learning-based super-resolution module
Adds the module plus loading classes for SR data
Complete with docs, tutorials and tests.
* Fix typo
* Small commit to restart buildbot
* Change refs from arXiv to official
* Remove video string
* dnn_superres: update perf test code
* dnn_superres: test fixup
-**datasets**: Datasets Reader -- Code for reading existing computer vision databases and samples of using the readers to train, test and run using that dataset's data.
24
24
25
-
-**dnn_objdetect**: Object Detection using CNNs -- Implements compact CNN Model for object detection. Trained using Caffe but uses opencv_dnn modeule.
25
+
-**dnn_objdetect**: Object Detection using CNNs -- Implements compact CNN Model for object detection. Trained using Caffe but uses opencv_dnn module.
26
26
27
27
-**dnn_superres**: Superresolution using CNNs -- Contains four trained convolutional neural networks to upscale images.
"General-100 dataset contains 100 bmp-format images (with no compression).
473
+
We used this dataset in our FSRCNN ECCV 2016 paper. The size of these 100 images ranges from 710 x 704 (large) to 131 x 112 (small).
474
+
They are all of good quality with clear edges but fewer smooth regions (e.g., sky and ocean), thus are very suitable for the super-resolution training.":
0 commit comments