{"url":"/dataset/alpha-matte-mfif-dataset","name":"alpha-matte MFIF dataset","full_name":"alpha-matte multi-focus image fusion dataset","description_markdown":"A large-scale training dataset suffering from the defocus spread effect (DSE) is synthesized by applying an $\\alpha$-matte boundary defocus model to the VOC 2012 dataset.\n\nMotivation: Due to the lack of large-scale datasets of multi-focus images, several data generation methods based on public natural image datasets have been adopted in many deep learning (DL)-based multi-focus image fusion algorithms. However, the DSE is neglected in all the abovementioned datasets. This unrealistic training data may limit the performance of these algorithms.\n\nApplication: For training DL-based multi-focus image fusion algorithms.","description_withheld":null,"homepage":"","introduced_date":"2020-09-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/mfif-gan-a-new-generative-adversarial-network","title":"MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion","first_author":"Yicheng Wang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["alpha-matte MFIF dataset"],"data_loaders":[],"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":"2026-09-24T18:15:14+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."}