Table 1: Quantitative denoising comparison among the proposed method (), the TGVSNR method, and the NRSNR method. The best values of SNR (db) of each row are displayed in bold.

ImageNoise indexProposed methodTGVSNRNRSNR
SNRSNRSNRSNR

House312.1511.1810.80
914.6513.9213.74
1515.8115.2515.17
2116.7916.1516.04

Livingroom37.637.286.65
99.729.439.05
1510.7510.5310.30
2111.4611.2911.06

Lena310.6510.139.72
913.3512.7312.48
1514.4614.0113.82
2115.4414.8914.69

Cameraman311.2711.0511.16
913.8713.3913.71
1515.0214.6014.91
2115.8615.4015.75

Boat38.448.127.80
910.3810.1110.25
1511.6011.4711.56
2112.4012.2412.40

Nimes34.373.964.02
96.806.266.26
158.037.567.53
219.008.458.42

Phantom312.3810.7212.08
916.6714.5116.09
1518.7616.6318.28
2120.4117.9219.61

Fields310.3810.089.50
912.0711.9011.56
1513.0612.8912.68
2113.8513.5613.41

Peppers311.4610.9010.07
913.9613.5713.01
1515.0514.8614.40
2116.0315.6915.21

Rem138.187.977.91
910.4710.2210.37
1511.7011.4711.65
2112.5312.3012.47