{"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/flowie-efficient-image-enhancement-via","title":"FlowIE: Efficient Image Enhancement via Rectified Flow","arxiv_id":"2406.00508","date":"2024-06-01","proceeding":"CVPR 2024 1","authors":["Yixuan Zhu","Wenliang Zhao","Ao Li","Yansong Tang","Jie zhou","Jiwen Lu"],"abstract":"Image enhancement holds extensive applications in real-world scenarios due to complex environments and limitations of imaging devices. Conventional methods are often constrained by their tailored models, resulting in diminished robustness when confronted with challenging degradation conditions. In response, we propose FlowIE, a simple yet highly effective flow-based image enhancement framework that estimates straight-line paths from an elementary distribution to high-quality images. Unlike previous diffusion-based methods that suffer from long-time inference, FlowIE constructs a linear many-to-one transport mapping via conditioned rectified flow. The rectification straightens the trajectories of probability transfer, accelerating inference by an order of magnitude. This design enables our FlowIE to fully exploit rich knowledge in the pre-trained diffusion model, rendering it well-suited for various real-world applications. Moreover, we devise a faster inference algorithm, inspired by Lagrange's Mean Value Theorem, harnessing midpoint tangent direction to optimize path estimation, ultimately yielding visually superior results. Thanks to these designs, our FlowIE adeptly manages a diverse range of enhancement tasks within a concise sequence of fewer than 5 steps. Our contributions are rigorously validated through comprehensive experiments on synthetic and real-world datasets, unveiling the compelling efficacy and efficiency of our proposed FlowIE. Code is available at https://github.com/EternalEvan/FlowIE.","url_abs":"https://arxiv.org/abs/2406.00508v1","url_pdf":"https://arxiv.org/pdf/2406.00508v1.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":"flowie-efficient-image-enhancement-via","repo_url":"https://github.com/eternalevan/flowie","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.00508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00508"}},"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/eternalevan/flowie","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/EternalEvan/FlowIE","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_fixture":2,"ran":3,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":6,"ran":6,"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":0,"samples":[{"code_sha256_prefix":"144d10b49baeb8a6","entry":"window_partition","repo":"EternalEvan/FlowIE","repo_kind":"official","path":"model/swinir.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/model/swinir.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"144d10b49baeb8a6"}},{"code_sha256_prefix":"503dc92a5460ffce","entry":"calculate_psnr","repo":"EternalEvan/FlowIE","repo_kind":"official","path":"evaluate.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/evaluate.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"503dc92a5460ffce"}},{"code_sha256_prefix":"050b310f4472034c","entry":"calculate_ssim","repo":"EternalEvan/FlowIE","repo_kind":"official","path":"evaluate.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/evaluate.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"050b310f4472034c"}},{"code_sha256_prefix":"c54a649ce031959d","entry":"forward_flowie_one_step","repo":"EternalEvan/FlowIE","repo_kind":"official","path":"inference_bsr.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/inference_bsr.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c54a649ce031959d"}},{"code_sha256_prefix":"ea9dbc131adf582e","entry":"space_timesteps","repo":"EternalEvan/FlowIE","repo_kind":"official","path":"model/spaced_sampler.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/model/spaced_sampler.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ea9dbc131adf582e"}},{"code_sha256_prefix":"61bf152e6a42a184","entry":"window_reverse","repo":"EternalEvan/FlowIE","repo_kind":"official","path":"model/swinir.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/model/swinir.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"61bf152e6a42a184"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}