{"id":"W4388399456","doi":"10.20944/preprints202311.0231.v1","title":"Single Image Super Resolution using Deep Residual Learning","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Residual; Artificial intelligence; Computer science; Deep learning; Convolution (computer science); Interpolation (computer graphics); Transpose; Sampling (signal processing); Image quality; Image (mathematics); Pattern recognition (psychology); Computer vision; Machine learning; Algorithm; Artificial neural network; Filter (signal processing)","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":[0.0005363796,0.0006048073,0.0006347292,0.000549075,0.0001381957,0.0005706123,0.0007680496,0.0005856437,0.001825281],"category_scores_gemma":[0.001126537,0.0003143525,0.000681463,0.0004872992,0.0003206979,0.0009553509,0.0007095415,0.000933654,0.0006950221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005017251,"about_ca_system_score_gemma":0.0005091295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003213026,"about_ca_topic_score_gemma":0.003995095,"domain_scores_codex":[0.9998179,0.00002696625,0.000007896348,0.00004226515,0.00008212159,0.00002290447],"domain_scores_gemma":[0.9997067,0.00009897005,0.00004070975,0.00006079728,0.00007353181,0.00001921167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002383671,0.0001088156,0.0005412557,0.0001376261,0.0001126033,0.0001284905,0.00005336136,0.5914423,0.05053427,0.007999603,0.002844878,0.3458584],"study_design_scores_gemma":[0.000002401414,0.00001844611,0.00005422062,0.000003559677,0.000004864064,0.00002864145,0.000002541299,0.9937544,0.004685303,0.001056157,0.0003861473,0.000003286448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02392726,0.0005067789,0.9719018,0.0001457169,0.00003566813,0.00003071081,0.0001043657,0.001336186,0.002011472],"genre_scores_gemma":[0.4624966,0.0009195558,0.5284569,0.0001913871,0.00004653334,0.0000658437,0.0006023263,0.0002370404,0.006983857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003213026,"threshold_uncertainty_score":0.006388605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2000484686478735,"score_gpt":0.3832797132569491,"score_spread":0.1832312446090756,"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."}}