{"id":"W4411284470","doi":"10.1007/s11042-025-20946-4","title":"HiSpecmer: a deep efficient image super resolution network using transformers with hierarchical and spectral feature attention","year":2025,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); Superresolution; Computer vision; Voltage; Electrical engineering","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":[0.0002778505,0.0007191048,0.0005294714,0.0004395768,0.0003133191,0.0006375815,0.001460601,0.0006001401,0.006215063],"category_scores_gemma":[0.0005476818,0.0003508222,0.000331569,0.0004400209,0.0003631036,0.001450355,0.001055992,0.0009366335,0.001700322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005004785,"about_ca_system_score_gemma":0.000711765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004768949,"about_ca_topic_score_gemma":0.01329965,"domain_scores_codex":[0.9999006,0.00001283722,0.000003945423,0.00003300228,0.00003292272,0.00001675142],"domain_scores_gemma":[0.9998562,0.00003522648,0.000009199197,0.00003856701,0.00003565991,0.00002519185],"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":[0.0005072227,0.000299807,0.0005341503,0.0002112226,0.0001746081,0.0002101005,0.00008882034,0.06988144,0.09970216,0.02733859,0.0366778,0.7643741],"study_design_scores_gemma":[0.0000424835,0.0001533922,0.0002713818,0.00001455603,0.00005147902,0.0001406175,0.0000241749,0.9306175,0.03897549,0.01906391,0.01061878,0.00002626689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01716552,0.0005024196,0.9694645,0.0002176175,0.0001374323,0.00008334414,0.0004463199,0.007115246,0.004867443],"genre_scores_gemma":[0.3521464,0.0007004981,0.6245139,0.0007194227,0.00009834477,0.000114489,0.001567714,0.0007134862,0.01942568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006215063,"threshold_uncertainty_score":0.02079147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00876387843133815,"score_gpt":0.2613785485364546,"score_spread":0.2526146701051165,"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."}}