It is known that visual noise added to sinusoidal gratings changes the typical U-shaped threshold curve which becomes flat in log-log scale for frequencies below 10c/deg when gratings are masked with white noise of high power spectral density level. These results have been explained using the critical-band-masking (CBM) model by supposing a visual filter-bank of constant relative bandwidth. However, some psychophysical and biological data support the idea of variable octave bandwidth. The CBM model has been used here to explain the progressive change of threshold curves with the noise mask level and to estimate the bandwidth of visual filters. Bayesian staircases were used in a 2IFC paradigm to measure contrast thresholds of horizontal sinusoidal gratings (0.25-8 c/deg) within a fixed Gaussian window and masked with one-dimensional, static, broadband white noise with each of five power density levels. Raw data showed that the contrast threshold curve progressively shifts upward and flattens out as the mask noise level increases. Theoretical thresholds from the CBM model were fitted simultaneously to the data at all five noise levels using visual filters with log-Gaussian gain functions. If we assume a fixed-channel detection model, the best fit was obtained when the octave bandwidth of visual filters decreases as a function of peak spatial frequency.