复数域幅相协同的轻量频域超分辨率网络

崔鹏,高闯,孟庆涵

Lightweight frequency-domain super-resolution network based on amplitude-phase collaboration in complex domain

Peng CUI,Chuang GAO,Qinghan MENG

表 6 不同尺度因子的超分辨率重建模型在各测试集上的峰值信噪比与结构相似性指标对比结果

Tab.6 Comparison of PSNR and SSIM indicator of super-resolution reconstruction models with different scale factors on various test sets

方法 尺度 因子 Np/103 FLOPs/109 Set5 Set14 B100 Urban100 Manga109 PSNR/dB SSIM PSNR/dB SSIM PSNR/dB SSIM PSNR/dB SSIM PSNR/dB SSIM DBNet[34] 2 416 84.15 38.00 0.9602 33.43 0.9173 32.22 0.9007 32.07 0.9273 39.00 0.9775 LKDN[21] 2 291 66.60 38.06 0.9609 34.00 0.9207 32.28 0.9011 32.92 0.9350 39.12 0.9779 RepRFN[35] 2 386 85.12 38.07 0.9612 33.63 0.9184 32.22 0.9009 32.10 0.9274 39.00 0.9774 SAFMN[33] 2 228 52.00 38.00 0.9611 33.54 0.9177 32.16 0.8995 31.84 0.9256 38.71 0.9771 OSFFNet[36] 2 516 83.20 38.10 0.9601 33.72 0.9190 32.29 0.9012 32.67 0.9331 39.09 0.9780 EARFA[37] 2 1026 229.0 38.04 0.9614 33.98 0.9212 32.32 0.9012 32.88 0.9359 39.16 0.9782 DiMoSR[38] 2 338 76.00 38.06 0.9607 33.74 0.9194 32.24 0.9006 32.30 0.9295 38.05 0.9782 DSCLoRA[39] 2 398 77.00 38.02 0.9607 33.72 0.9212 32.26 0.8992 32.81 0.9290 38.01 0.9678 IBMDB[40] 2 622 92.83 38.06 0.9673 33.66 0.9174 32.16 0.8992 32.17 0.9277 38.81 0.9772 本文方法 2 306 21.00 38.15 0.9612 34.04 0.9218 32.35 0.9015 32.95 0.9353 39.16 0.9783 DBNet[34] 3 826 70.20 34.46 0.9279 30.42 0.8427 29.18 0.8063 28.51 0.8571 33.99 0.9466 LKDN[21] 3 311 31.40 34.54 0.9285 30.52 0.8455 29.21 0.8078 28.50 0.8601 34.08 0.9475 RepRFN[35] 3 392 38.40 34.45 0.9280 30.39 0.8430 29.13 0.8068 28.06 0.8494 33.76 0.9451 SAFMN[33] 3 233 23.00 34.34 0.9267 30.33 0.8418 29.08 0.8048 27.95 0.8474 33.52 0.9437 OSFFNet[36] 3 524 37.80 34.58 0.9287 30.48 0.8450 29.21 0.8078 28.50 0.8601 34.08 0.9475 EARFA[37] 3 1034 102.40 34.53 0.9277 30.51 0.8451 29.29 0.8083 28.87 0.8632 34.18 0.9464 DiMoSR[38] 3 — 45.00 34.46 0.9279 30.44 0.8429 29.20 0.8073 28.51 0.8574 33.89 0.9469 DSCLoRA[39] 3 416 25.83 34.51 0.9281 30.43 0.8444 29.06 0.8066 28.33 0.8563 34.04 0.9436 IBMDB[40] 3 991 67.54 34.45 0.9276 30.44 0.8445 29.15 0.8065 28.37 0.8563 34.08 0.9439 本文方法 3 310 21.00 34.59 0.9286 30.54 0.8455 29.28 0.8081 28.87 0.8631 34.18 0.9476 DBNet[34] 4 832 51.80 32.29 0.8961 28.71 0.7834 27.66 0.7377 26.34 0.7909 30.83 0.9111 LKDN[21] 4 322 18.30 32.39 0.8979 28.79 0.7859 27.69 0.7402 26.40 0.7965 30.43 0.9140 RepRFN[35] 4 402 22.10 32.28 0.8969 28.68 0.7836 27.65 0.7389 26.18 0.7858 30.79 0.9102 SAFMN[33] 4 240 14.00 32.18 0.8948 28.60 0.7813 27.58 0.7359 25.97 0.7809 30.89 0.9063 OSFFNet[36] 4 537 22.00 32.39 0.8976 28.75 0.7852 27.66 0.7393 26.36 0.7950 30.84 0.9125 EARFA[37] 4 1209 11.61 32.38 0.8975 28.76 0.7859 27.65 0.7431 26.60 0.8004 31.07 0.9147 DiMoSR[38] 4 349 20.00 32.31 0.8962 28.74 0.7844 27.65 0.7384 26.25 0.7889 30.89 0.9061 DSCLoRA[39] 4 426 27.87 32.19 0.8958 28.63 0.7830 27.61 0.7370 26.13 0.7864 30.60 0.9095 IBMDB[40] 4 917 43.56 32.23 0.8954 28.61 0.7812 27.55 0.7356 26.10 0.7856 30.71 0.9073 本文方法 4 318 21.00 32.44 0.8979 28.83 0.7864 27.69 0.7428 26.60 0.8004 31.09 0.9146