Blind video denoising via texture-aware noise estimation

Noise level is an important parameter for the design of video denoising algorithms in video processing applications. However, in practice, the noise level is unknown in most cases, but most existing denoising algorithms simply assume that the noise level is known beforehand, which severely limits their practical use. In this paper, we propose a novel blind video denoising algorithm via block-based optimal noise estimation that adaptively measures noise level by the principal component analysis. The adjacent frame images are searched to construct similar blocks by the block-matching method. The inter-frame differences of these similar blocks are used to estimate the video noise for the impact suppression of video motion, where the video noise is verified to comply with the normal distribution. The weak texture regions are selected by the thresholding function that is deduced based on the normal distribution. In addition, the proposed noise estimation approach is separately combined with several current popular non-blind video denoising methods to verify its superiority. Experimental results demonstrate that the proposed algorithm with low computational complexity not only has better estimation results, but also outperforms the state-of-the-art methods in most cases.