{"url":"/dataset/pixelshift200","name":"PixelShift200","full_name":null,"description_markdown":"Advanced pixel shift technology is employed to perform a full color sampling of the image. Pixel shift technology takes four samples of the same image at nearly the same time, and physically controls the camera sensor to move one pixel horizontally or vertically at each sampling to capture all color information at each pixel. The pixel shift technology ensures that the sampled images follow the distribution of natural images sampled by the camera, and the full information of the color (R, Gr, Gb, B channel) is completely obtained without any need of interpolation. In this way, the collected RGB images are artifacts-free, which leads to better training results for demosaicing related tasks.\r\n\r\nPixelShift200 Dataset contains 210 high quality 4K images.\r\n\r\n- Training: 200 images\r\n- Testing: 10 images\r\n- Key Features: fully colored, demosiacing artifacts free\r\n- Camera: SONY α7R III\r\n\r\nSource: [PixelShift200](https://guochengqian.github.io/project/pixelshift200/#overview)","description_withheld":null,"homepage":"https://guochengqian.github.io/project/pixelshift200/#overview","introduced_date":"2019-05-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/trinity-of-pixel-enhancement-a-joint-solution","title":"Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline","first_author":"Guocheng Qian","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Super-Resolution","url":"/task/super-resolution","datasets_with_task":"/datasets/task/super-resolution"},{"name":"Denoising","url":"/task/denoising","datasets_with_task":"/datasets/task/denoising"},{"name":"Demosaicking","url":"/task/demosaicking","datasets_with_task":"/datasets/task/demosaicking"}],"languages":[],"variants":["PixelShift200"],"data_loaders":[{"repo":"https://github.com/guochengqian/TENet","url":"https://github.com/guochengqian/TENet/tree/master/datasets","frameworks":["pytorch"]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}