{"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/essaformer-efficient-transformer-for","title":"ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution","arxiv_id":"2307.14010","date":"2023-07-26","proceeding":"ICCV 2023 1","authors":["Mingjin Zhang","Chi Zhang","Qiming Zhang","Jie Guo","Xinbo Gao","Jing Zhang"],"abstract":"Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. However, the prevailing CNN-based approaches have shown limitations in building long-range dependencies and capturing interaction information between spectral features. This results in inadequate utilization of spectral information and artifacts after upsampling. To address this issue, we propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure. Specifically, we first introduce a robust and spectral-friendly similarity metric, \\ie, the spectral correlation coefficient of the spectrum (SCC), to replace the original attention matrix and incorporates inductive biases into the model to facilitate training. Built upon it, we further utilize the kernelizable attention technique with theoretical support to form a novel efficient SCC-kernel-based self-attention (ESSA) and reduce attention computation to linear complexity. ESSA enlarges the receptive field for features after upsampling without bringing much computation and allows the model to effectively utilize spatial-spectral information from different scales, resulting in the generation of more natural high-resolution images. Without the need for pretraining on large-scale datasets, our experiments demonstrate ESSA's effectiveness in both visual quality and quantitative results.","url_abs":"https://arxiv.org/abs/2307.14010v1","url_pdf":"https://arxiv.org/pdf/2307.14010v1.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":"essaformer-efficient-transformer-for","repo_url":"https://github.com/rexzhan/essaformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"hyperspectral-image-super-resolution","task_name":"Hyperspectral Image Super-Resolution"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"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":{"atlas_url":"https://app.syntology.ai/?focus=2307.14010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.14010"}},"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/rexzhan/essaformer","reach":null}],"summary":{"ran":7,"unverified":2},"by_repo_kind":{"official":{"samples":9,"ran":7,"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":"cdce096003c299d8","entry":"Convdown","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"cdce096003c299d8"}},{"code_sha256_prefix":"64ec684038d635df","entry":"Convup","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"64ec684038d635df"}},{"code_sha256_prefix":"4dd28ee316a86d05","entry":"Downsample","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"4dd28ee316a86d05"}},{"code_sha256_prefix":"0b8c1531d44f9b73","entry":"ESSAttn","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"0b8c1531d44f9b73"}},{"code_sha256_prefix":"a2239f95809bdfc5","entry":"PatchEmbed","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"a2239f95809bdfc5"}},{"code_sha256_prefix":"52a009960f8e40d1","entry":"PatchUnEmbed","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"52a009960f8e40d1"}},{"code_sha256_prefix":"da853ba463b06598","entry":"Upsample","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"da853ba463b06598"}},{"code_sha256_prefix":"238469839cef1b0c","entry":"ESSA","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"238469839cef1b0c"}},{"code_sha256_prefix":"9d9134dafd1f7730","entry":"blockup","repo":"rexzhan/essaformer","repo_kind":"official","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"mcp_get_code":{"code_sha256":"9d9134dafd1f7730"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}