{"id":"W2106940366","doi":"10.1109/igarss.2003.1295352","title":"Multi- spectral image resolution refinement using stationary wavelet transform","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Panchromatic film; Image fusion; Wavelet; Wavelet transform; Artificial intelligence; Computer science; Image resolution; Computer vision; Pixel; Resolution (logic); Image (mathematics); Pattern recognition (psychology); Stationary wavelet transform; Multiresolution analysis; Image processing; Wavelet packet decomposition","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005617573,0.0001231349,0.00009181791,0.00008731164,0.00005631967,0.00001415618,0.00006582173,0.00003991641,0.0002239487],"category_scores_gemma":[0.000007101447,0.000123326,0.00003855375,0.0001209588,0.00002572581,0.0003128023,0.00001098331,0.0001081082,0.00002380778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003203526,"about_ca_system_score_gemma":0.00001657674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005153889,"about_ca_topic_score_gemma":0.00003646347,"domain_scores_codex":[0.9993205,0.000004473712,0.0002008303,0.0001286585,0.0001270617,0.0002184619],"domain_scores_gemma":[0.9997674,0.000006030788,0.00001416905,0.0001349593,0.00003149215,0.0000459868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009461959,0.00005557216,0.000003275191,0.00004169995,0.00001177337,0.00002210157,0.0002067838,0.03671703,0.9523665,0.000819199,0.0005166614,0.009229992],"study_design_scores_gemma":[0.001003876,0.000058395,0.0005848295,0.0000469867,0.00001064973,0.00002612791,0.0001333108,0.1526099,0.8390862,0.003422715,0.002661516,0.0003554862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02114353,0.00006093689,0.9719695,0.0001237828,0.00006709027,0.000219341,0.00001372074,0.001039062,0.005362989],"genre_scores_gemma":[0.1204384,0.00007223386,0.8791959,0.00004470265,0.00003240805,0.00001259359,0.0000212977,0.00003061918,0.0001517639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1158928,"threshold_uncertainty_score":0.5029088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643244415887908,"score_gpt":0.2667910657622425,"score_spread":0.2503586216033634,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}