Efficient image sensor noise estimation via iterative re-weighted least squares
Noise estimation is crucial in many image processing algorithms such as image denoising. Conventionally, the noise is assumed as signal-independent additive white Gaussian process. However, for the real raw-data of imaging sensors, the present noise is better modeled as signal-dependent noise. In this work, we propose an efficient image sensor noise estimation method based on iterative re-weighted least squares optimization. Specifically, the image patches are first clustered into different groups, each of which will generate a data sample. To fit those observations robustly, we introduce a weighting matrix to reflect the credibility of each sample. Unfortunately, this setting of weighting matrix in turn depends on the unknown noise parameters. We then develop an iterative re-weighted least squares optimization procedure, in which the weighting matrix and parameter estimates can be updated alternately. Experimental results show that our method outperforms the state-of-the-art works, in terms of both estimation accuracy and computational efficiency.