Although state-of-the-art image denoising algorithms have achieved outstanding results, removing real, color noise from a single image remains a challenging problem. An adaptive image denoising algorithm that integrates a constant time bilateral filter with noise-level estimation is proposed. The estimation of the noise-level function (NLF), which describes the noise level as a function of image brightness, is the key to ensure the removal of the color noise. To achieve this aim, a bilateral median filter is exploited to estimate the upper bound of NLF by fitting a lower envelope to the standard deviations of per-segment image variances. Furthermore, we make an empirical study on the relationship between the optimal parameter of constant time bilateral filter and the noise level. Then, an adaptive denoising algorithm, where the filtering parameter is automatically adjusted according to the estimated noise level, is conducted to obtain the underlying clean image from the noisy input. In addition, we present a new method of synthesizing noise, where the synthetic noise is very close to the real noise. Meanwhile, we test our algorithm on the synthetic noise images and on the real applications as well. Various experimental results show that our algorithm outperforms state-of-the-art denoising algorithms in eliminating real, color noise.