Moire Photo Benchmark The uncontaminated reference images in our benchmark come from the validation images and testing images of ImageNet ISVRC 2012 dataset (http://www.image-net.org/challenges/LSVRC/2012/). PLease download it from https://drive.google.com/drive/folders/109cAIZ0ffKLt34P7hOMKUO14j3gww2UC?usp=sharing Note that: 1. We do not own the copy right of this database and you should obey the rules of ImageNet when using this database. 2. Researcher shall use the Database only for non-commercial research and educational purposes. 3. Use this dataset for your own risk. ----------------------------------------------------------------------------------------------------------------------- Please cite the following paper if you use this dataset: Sun, Yujing, Yizhou Yu, and Wenping Wang. "Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks." IEEE Transactions on Image Processing 27.8 (2018): 4160-4172. ----------------------------------------------------------------------------------------------------------------------- <1. Summary> Of all the pairs of images, 90% is used as the traning data and 10% is used for validation and testing. The images in folder source and folder target are registered as we stated in the paper. <2. Image Naming> Images in this benchmark are named as image_class_partX_Y_type.png class: test/val (test and val means that this image is from the testing and validation image of ImageNet ISVRC 2012,respectively.) X: 001-011 for class test and 001-010 for class val Y: the index of this image type: source/target (source means this is a contaminated image and target means this is a clean reference image.) <3. Capture Device Specifications> Phone Models: (1)iphone6 (2) Samsung Galaxy S7 Edge (3) Sony Xperia Z5 Premium Dual Display Monitors: (1) Dell U2410 1920 (2) DELL SE198WFP 1280 (3) Macbook Pro Retina (Mid 2014) 2560 phoneMode (1) and Display Monitor(1): val_part001, val_part002, val_part007 phoneMode (1) and Display Monitor(2): val_part003, val_part004, val_part008 phoneMode (1) and Display Monitor(3): test_part001, val_part010 phoneMode (2) and Display Monitor(1): test_part003, test_part008 phoneMode (2) and Display Monitor(2): test_part002, test_part007 phoneMode (2) and Display Monitor(3): val_part005, val_part006, val_part009 phoneMode (3) and Display Monitor(1): test_part004, test_part009 phoneMode (3) and Display Monitor(2): test_part005, test_part010 phoneMode (3) and Display Monitor(3): test_part006, test_part011