{"id":"W3177140187","doi":"10.1609/aaai.v35i2.16266","title":"Deep Low-Contrast Image Enhancement using Structure Tensor Representation","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Contrast (vision); Structure tensor; Image (mathematics); Computer science; Artificial intelligence; Representation (politics); Ground truth; Tensor (intrinsic definition); Pattern recognition (psychology); Function (biology); Deep learning; Image quality; Mathematics; Geometry","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":[0.0006739355,0.001065878,0.0007187257,0.0005861279,0.0002224053,0.0009588188,0.001069568,0.0007901652,0.002053603],"category_scores_gemma":[0.001230521,0.0003635324,0.0007789328,0.0003922109,0.0005938081,0.001637288,0.001174882,0.001711333,0.0006821245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006010064,"about_ca_system_score_gemma":0.0005334319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168639,"about_ca_topic_score_gemma":0.002429582,"domain_scores_codex":[0.9997745,0.00003425396,0.000009135814,0.00004626003,0.00009686753,0.00003884823],"domain_scores_gemma":[0.9996617,0.00009350967,0.00005739686,0.00006945961,0.00008816968,0.00002971616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003381053,0.000208213,0.001082041,0.0003608593,0.0001429494,0.0002478742,0.000127155,0.2968374,0.2044745,0.01701546,0.00493963,0.4742258],"study_design_scores_gemma":[0.00001095184,0.0000871583,0.000271765,0.00001741826,0.00002641041,0.0001277952,0.000008666517,0.9444962,0.04847155,0.004091179,0.002377149,0.00001381154],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01346575,0.0004352387,0.9832299,0.0001492951,0.00004074439,0.0000395092,0.00005824205,0.001108433,0.001472816],"genre_scores_gemma":[0.3272787,0.0009413912,0.6628031,0.0003614803,0.00008248653,0.00009287911,0.0004226626,0.0003548549,0.007662553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002053603,"threshold_uncertainty_score":0.006869912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05306238292628465,"score_gpt":0.3160405930976323,"score_spread":0.2629782101713477,"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."}}