{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hallucinating-very-low-resolution-unaligned","title":"Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders","arxiv_id":null,"date":"2017-07-01","proceeding":"CVPR 2017 7","authors":["Xin Yu","Fatih Porikli"],"abstract":"Most of the conventional face hallucination methods assume the input image is sufficiently large and aligned, and all require the input image to be noise-free. Their performance degrades drastically if the input image is tiny, unaligned, and contaminated by noise.   In this paper, we introduce a novel transformative discriminative autoencoder to 8X super-resolve unaligned noisy and tiny (16X16) low-resolution face images. In contrast to encoder-decoder based autoencoders, our method uses decoder-encoder-decoder networks. We first employ a transformative discriminative decoder network to upsample and denoise simultaneously. Then we use a transformative encoder network to project the intermediate HR faces to aligned and noise-free LR faces. Finally, we use the second decoder to generate hallucinated HR images. Our extensive evaluations on a very large face dataset show that our method achieves superior hallucination results and outperforms the state-of-the-art by a large margin of 1.82dB PSNR.\r","url_abs":"http://openaccess.thecvf.com/content_cvpr_2017/html/Yu_Hallucinating_Very_Low-Resolution_CVPR_2017_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Hallucinating_Very_Low-Resolution_CVPR_2017_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"face-hallucination","task_name":"Face Hallucination"},{"task_slug":"hallucination","task_name":"Hallucination"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-super-resolution-on-vggface2-8x","task":"Image Super-Resolution","dataset":"VggFace2 - 8x upscaling","model":"TDAE","rank_in_archive_order":7,"of":7,"metrics":{"PSNR":"20.19"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-webface-8x","task":"Image Super-Resolution","dataset":"WebFace - 8x upscaling","model":"TDAE","rank_in_archive_order":7,"of":7,"metrics":{"PSNR":"20.24"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}