{"id":"https://openalex.org/W4415709199","doi":"https://doi.org/10.1109/icme59968.2025.11209946","title":"Clouds and Haze Co-Removal Based on Saliency-Guided Multi-Scale Diffusion Model for Remote Sensing Images","display_name":"Clouds and Haze Co-Removal Based on Saliency-Guided Multi-Scale Diffusion Model for Remote Sensing Images","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415709199","doi":"https://doi.org/10.1109/icme59968.2025.11209946"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100688796","display_name":"Jingxuan Zhang","orcid":"https://orcid.org/0009-0005-5770-1128"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingxuan Zhang","raw_affiliation_strings":["Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087779583","display_name":"Libao Zhang","orcid":"https://orcid.org/0000-0002-0888-2330"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libao Zhang","raw_affiliation_strings":["Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100688796"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3123978,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.6108999848365784,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.6108999848365784,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.20759999752044678,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.07590000331401825,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/haze","display_name":"Haze","score":0.6237999796867371},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5917999744415283},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4909999966621399},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4092000126838684},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.40619999170303345},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3970000147819519},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3596000075340271},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.35530000925064087}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6955000162124634},{"id":"https://openalex.org/C79974267","wikidata":"https://www.wikidata.org/wiki/Q643546","display_name":"Haze","level":2,"score":0.6237999796867371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.614300012588501},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5917999744415283},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5245000123977661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.510200023651123},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.35499998927116394},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.35199999809265137},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2696000039577484},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.259799987077713},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2052257859","https://openalex.org/W2095396982","https://openalex.org/W2102166818","https://openalex.org/W2128254161","https://openalex.org/W2803003377","https://openalex.org/W2897456077","https://openalex.org/W2910101086","https://openalex.org/W2944396128","https://openalex.org/W2962793481","https://openalex.org/W2969663607","https://openalex.org/W2972981284","https://openalex.org/W2990007814","https://openalex.org/W3012965216","https://openalex.org/W3034595214","https://openalex.org/W3155072588","https://openalex.org/W4212840128","https://openalex.org/W4310459044","https://openalex.org/W4379184889","https://openalex.org/W4385757756","https://openalex.org/W4386065404","https://openalex.org/W4390908076","https://openalex.org/W4391235300","https://openalex.org/W4391807649","https://openalex.org/W4392909593","https://openalex.org/W4396777688","https://openalex.org/W4400433405","https://openalex.org/W4401051899","https://openalex.org/W4402716213"],"related_works":[],"abstract_inverted_index":{"Clouds":[0],"and":[1,20,54,82,106,154],"haze":[2,55],"co-removal":[3],"from":[4],"remote":[5],"sensing":[6],"images":[7],"is":[8,44,63,95,135],"an":[9,58],"important":[10],"task.":[11],"However,":[12],"current":[13],"algorithms":[14],"often":[15],"struggle":[16],"with":[17],"complex":[18],"distributions":[19],"uneven":[21],"illumination.":[22],"To":[23],"solve":[24],"the":[25,39,48,72,80,86,118,124,139,146],"issues,":[26],"we":[27],"propose":[28],"a":[29,67,99,128],"multi-scale":[30,100],"diffusion":[31],"model":[32,73],"guided":[33],"by":[34],"global":[35,40],"perceptual":[36,41],"saliency.":[37],"Firstly,":[38],"saliency":[42,69,93],"block":[43,62],"employed":[45],"to":[46,51,65,74,110,114,121,137],"enhance":[47],"model\u2019s":[49,119,140],"ability":[50],"perceive":[52],"clouds":[53,81],"distributions.":[56],"Then,":[57],"edge":[59],"feature":[60],"extraction":[61],"utilized":[64],"generate":[66],"texture":[68,77,92],"map,":[70],"guiding":[71,101],"focus":[75],"on":[76,132,152],"information":[78,112],"outside":[79],"haze,":[83],"thereby":[84],"improving":[85],"image":[87,115],"structure":[88],"generation":[89],"capability.":[90],"The":[91],"map":[94],"then":[96],"projected":[97],"into":[98],"network,":[102],"where":[103],"gray-scale":[104],"conversion":[105],"down-sampling":[107],"are":[108],"applied":[109],"suppress":[111],"unrelated":[113],"structure,":[116],"enhancing":[117],"robustness":[120],"variations":[122],"in":[123],"input":[125],"domain.":[126],"Finally,":[127],"loss":[129],"function":[130],"based":[131],"regressor-constrained":[133],"output":[134],"designed":[136],"optimize":[138],"performance.":[141],"Extensive":[142],"experiments":[143],"demonstrate":[144],"that":[145],"proposed":[147],"method":[148],"outperforms":[149],"state-of-the-art":[150],"methods":[151],"synthetic":[153],"real-world":[155],"images.":[156]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-30T00:00:00"}
