{"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/misalignment-robust-frequency-distribution","title":"Misalignment-Robust Frequency Distribution Loss for Image Transformation","arxiv_id":"2402.18192","date":"2024-02-28","proceeding":"CVPR 2024 1","authors":["Zhangkai Ni","Juncheng Wu","Zian Wang","Wenhan Yang","Hanli Wang","Lin Ma"],"abstract":"This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely aligned paired datasets with pixel-level alignments. However, creating precisely aligned paired images presents significant challenges and hinders the advancement of methods trained on such data. To overcome this challenge, this paper introduces a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Specifically, we transform image features into the frequency domain using Discrete Fourier Transformation (DFT). Subsequently, frequency components (amplitude and phase) are processed separately to form the FDL loss function. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain. Extensive experimental evaluations, focusing on image enhancement and super-resolution tasks, demonstrate that FDL outperforms existing misalignment-robust loss functions. Furthermore, we explore the potential of our FDL for image style transfer that relies solely on completely misaligned data. Our code is available at: https://github.com/eezkni/FDL","url_abs":"https://arxiv.org/abs/2402.18192v1","url_pdf":"https://arxiv.org/pdf/2402.18192v1.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":[{"paper_slug":"misalignment-robust-frequency-distribution","repo_url":"https://github.com/eezkni/fdl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.18192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18192"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/eezkni/fdl","reach":null}],"summary":{"ran":4,"unverified":1},"by_repo_kind":{"official":{"samples":5,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"722354fe8a744793","entry":"EffNet","repo":"eezkni/fdl","repo_kind":"official","path":"FDL_pytorch/FDL.py","file_url":"https://github.com/eezkni/fdl/blob/HEAD/FDL_pytorch/FDL.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"722354fe8a744793"}},{"code_sha256_prefix":"4a7177629eea76fe","entry":"Inception","repo":"eezkni/fdl","repo_kind":"official","path":"FDL_pytorch/FDL.py","file_url":"https://github.com/eezkni/fdl/blob/HEAD/FDL_pytorch/FDL.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4a7177629eea76fe"}},{"code_sha256_prefix":"c4c16f57ff8ebfaf","entry":"ResNet","repo":"eezkni/fdl","repo_kind":"official","path":"FDL_pytorch/FDL.py","file_url":"https://github.com/eezkni/fdl/blob/HEAD/FDL_pytorch/FDL.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c4c16f57ff8ebfaf"}},{"code_sha256_prefix":"eb7d7254bc5ad5c8","entry":"VGG","repo":"eezkni/fdl","repo_kind":"official","path":"FDL_pytorch/FDL.py","file_url":"https://github.com/eezkni/fdl/blob/HEAD/FDL_pytorch/FDL.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"eb7d7254bc5ad5c8"}},{"code_sha256_prefix":"273e3229ca4ad4f4","entry":"FDL_loss","repo":"eezkni/fdl","repo_kind":"official","path":"FDL_pytorch/FDL.py","file_url":"https://github.com/eezkni/fdl/blob/HEAD/FDL_pytorch/FDL.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"273e3229ca4ad4f4"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}