The MIT-Adobe FiveK dataset consists of 5,000 photographs taken with SLR cameras by a set of different photographers. They are all in RAW format; that is, all the information recorded by the camera sensor is preserved. We made sure that these photographs cover a broad range of scenes, subjects, and lighting conditions. We then hired five photography students in an art school to adjust the tone of the photos. Each of them retouched all the 5,000 photos using a software dedicated to photo adjustment (Adobe Lightroom) on which they were extensively trained. We asked the retouchers to achieve visually pleasing renditions, akin to a postcard. The retouchers were compensated for their work.
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This newly curated synthetic dataset specifies an additional reference region to guide image harmonization. There are 118,287 training images and 959 test images. The dataset consists of objects, backgrounds, and people.
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Read all the details about the dataset in our paper "NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement"