{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000327452,0.00008411772,0.0001638857,0.0002462746,0.00001436131,0.00001737319,0.00009522887,0.00003528819,0.000008770711],"category_scores_gemma":[0.00007386318,0.0000796353,0.00005367281,0.00005469881,0.00003587866,0.0001256128,0.00003170085,0.0001639406,5.502923e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009505745,"about_ca_system_score_gemma":0.00001180868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009503193,"about_ca_topic_score_gemma":0.000004689191,"domain_scores_codex":[0.999018,0.000006206897,0.0004912069,0.00005664661,0.0003298565,0.00009807647],"domain_scores_gemma":[0.9991685,0.00006882053,0.0002351889,0.0000656007,0.000413453,0.00004848909],"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":[0.00002673981,0.000005184762,0.00001690877,0.000007981674,0.00001458279,0.00003888242,0.00004688111,0.00008621763,0.5378001,0.000002094806,0.00005774162,0.4618967],"study_design_scores_gemma":[0.0003701617,0.00007307143,0.0007142235,0.000205002,0.000008324404,0.0001105088,0.00008145835,0.09186732,0.9051635,0.0003098478,0.001023092,0.00007348543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3408512,0.00009779968,0.6581855,0.00006584446,0.0003108257,0.00003129609,0.000001100185,0.00002546856,0.0004309771],"genre_scores_gemma":[0.6079264,0.00003314046,0.3919106,0.0000331793,0.0000815313,2.129898e-9,7.752048e-7,0.00001002405,0.000004399866],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4618232,"threshold_uncertainty_score":0.3247433,"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."}}