{"id":"W2891639959","doi":"10.1109/icassp.2018.8461664","title":"Edge-Based Loss Function for Single Image Super-Resolution","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Mean squared error; Computer science; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Image (mathematics); Pixel; Convolution (computer science); Image restoration; Image quality; Function (biology); Image resolution; Salient; Computer vision; Pattern recognition (psychology); Artificial neural network; Superresolution; Mathematics; Image processing; Statistics","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":[0.0001829569,0.0001091945,0.00008660385,0.00008770383,0.0002058626,0.000170577,0.0004150496,0.00005011519,0.00002444204],"category_scores_gemma":[0.000104838,0.0001000982,0.00004497221,0.0002544939,0.0001448685,0.001132769,0.00009185711,0.000048246,0.00005173213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007294022,"about_ca_system_score_gemma":0.00005730231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005574444,"about_ca_topic_score_gemma":0.000005061202,"domain_scores_codex":[0.9990963,0.00001775439,0.0001433509,0.0003528336,0.0001346772,0.0002550287],"domain_scores_gemma":[0.9990326,0.00005267965,0.00005400353,0.0004159476,0.0003976352,0.00004719155],"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":[0.00008194693,0.0002394746,0.00006094972,0.00005501416,0.000006861679,0.000002506071,0.00009831283,0.000006197909,0.8393642,0.02458093,0.02168669,0.1138169],"study_design_scores_gemma":[0.0003867236,0.0007081203,0.00005607939,0.00002444496,0.000005926261,0.000006915516,0.000004806096,0.3198216,0.6070079,0.04741409,0.02433612,0.000227345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002445782,0.00002802881,0.9930429,0.0009616937,0.000274446,0.0002015965,0.00000133409,0.001302584,0.003942783],"genre_scores_gemma":[0.1988857,3.159776e-7,0.7998762,0.0006709109,0.0001499205,0.00004104374,0.000003568546,0.00001085262,0.0003614584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3198154,"threshold_uncertainty_score":0.4081886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02239487850176264,"score_gpt":0.2807992077772113,"score_spread":0.2584043292754487,"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."}}