{"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/deep-optics-for-video-snapshot-compressive-1","title":"Deep Optics for Video Snapshot Compressive Imaging","arxiv_id":"2404.05274","date":"2024-04-08","proceeding":"ICCV 2023 1","authors":["Ping Wang","Lishun Wang","Xin Yuan"],"abstract":"Video snapshot compressive imaging (SCI) aims to capture a sequence of video frames with only a single shot of a 2D detector, whose backbones rest in optical modulation patterns (also known as masks) and a computational reconstruction algorithm. Advanced deep learning algorithms and mature hardware are putting video SCI into practical applications. Yet, there are two clouds in the sunshine of SCI: i) low dynamic range as a victim of high temporal multiplexing, and ii) existing deep learning algorithms' degradation on real system. To address these challenges, this paper presents a deep optics framework to jointly optimize masks and a reconstruction network. Specifically, we first propose a new type of structural mask to realize motion-aware and full-dynamic-range measurement. Considering the motion awareness property in measurement domain, we develop an efficient network for video SCI reconstruction using Transformer to capture long-term temporal dependencies, dubbed Res2former. Moreover, sensor response is introduced into the forward model of video SCI to guarantee end-to-end model training close to real system. Finally, we implement the learned structural masks on a digital micro-mirror device. Experimental results on synthetic and real data validate the effectiveness of the proposed framework. We believe this is a milestone for real-world video SCI. The source code and data are available at https://github.com/pwangcs/DeepOpticsSCI.","url_abs":"https://arxiv.org/abs/2404.05274v1","url_pdf":"https://arxiv.org/pdf/2404.05274v1.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":"deep-optics-for-video-snapshot-compressive-1","repo_url":"https://github.com/pwangcs/deepopticssci","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2404.05274","atlas_url":"https://app.syntology.ai/?focus=2404.05274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05274"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"deterministic:regex_extraction","url":"https://github.com/pwangcs/DeepOpticsSCI","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":0,"samples":[{"code_sha256_prefix":"671a3dc95519631b","entry":"BasicBlock","repo":"pwangcs/DeepOpticsSCI","repo_kind":"official","path":"model/digital_layer.py","file_url":"https://github.com/pwangcs/DeepOpticsSCI/blob/HEAD/model/digital_layer.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":"671a3dc95519631b"}},{"code_sha256_prefix":"7e43ae419883abce","entry":"CFormerBlock","repo":"pwangcs/DeepOpticsSCI","repo_kind":"official","path":"model/digital_layer.py","file_url":"https://github.com/pwangcs/DeepOpticsSCI/blob/HEAD/model/digital_layer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7e43ae419883abce"}},{"code_sha256_prefix":"ed2d331de6ccdd5c","entry":"ResTSA","repo":"pwangcs/DeepOpticsSCI","repo_kind":"official","path":"model/digital_layer.py","file_url":"https://github.com/pwangcs/DeepOpticsSCI/blob/HEAD/model/digital_layer.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":"ed2d331de6ccdd5c"}},{"code_sha256_prefix":"1821c8b8b6d00028","entry":"TimesAttention3D","repo":"pwangcs/DeepOpticsSCI","repo_kind":"official","path":"model/digital_layer.py","file_url":"https://github.com/pwangcs/DeepOpticsSCI/blob/HEAD/model/digital_layer.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":"1821c8b8b6d00028"}},{"code_sha256_prefix":"b57a2d73f0a49ab1","entry":"Res2former","repo":"pwangcs/DeepOpticsSCI","repo_kind":"official","path":"model/digital_layer.py","file_url":"https://github.com/pwangcs/DeepOpticsSCI/blob/HEAD/model/digital_layer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b57a2d73f0a49ab1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}