{"id":"W2105602976","doi":"10.1080/01431160701313826","title":"Comparison and improvement of wavelet‐based image fusion","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Wavelet transform; Artificial intelligence; Image fusion; Stationary wavelet transform; Discrete wavelet transform; Wavelet packet decomposition; Second-generation wavelet transform; Pattern recognition (psychology); Orthogonal wavelet; Biorthogonal wavelet; Lifting scheme; Computer vision; Computer science; Biorthogonal system; Transformation (genetics); Fusion; Mathematics; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004431924,0.0009139786,0.001140069,0.004169459,0.0004637769,0.001091142,0.0009947531,0.001055742,0.001062117],"category_scores_gemma":[0.007618723,0.0003291125,0.001552202,0.003344653,0.0004929448,0.002647139,0.001160426,0.0007300138,0.0004344077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007507768,"about_ca_system_score_gemma":0.0005486197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001779243,"about_ca_topic_score_gemma":0.0007751683,"domain_scores_codex":[0.9971935,0.0004872925,0.0001980005,0.0002313424,0.00175265,0.0001370935],"domain_scores_gemma":[0.997579,0.0006353148,0.000175944,0.0001928282,0.001374076,0.00004267452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008631246,0.0001517112,0.002819785,0.0008999091,0.0003655533,0.0001354447,0.0003034433,0.09031501,0.04219656,0.008334153,0.002445023,0.8511704],"study_design_scores_gemma":[0.0001070014,0.0008079961,0.01451813,0.0001976111,0.0004279579,0.0005505831,0.0003098691,0.8431672,0.1155308,0.005972271,0.01822929,0.0001813471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1518019,0.01480326,0.8171406,0.000630628,0.0005934144,0.0002009627,0.0002986126,0.001707846,0.01282284],"genre_scores_gemma":[0.6452889,0.01088845,0.3395217,0.0001508781,0.0002033143,0.0001499748,0.0008647176,0.0002383636,0.002693717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004431924,"threshold_uncertainty_score":0.02343851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000972699096003,"score_gpt":0.301327414056103,"score_spread":0.291317687065143,"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."}}