A spatially cohesive superpixel model for image noise level estimation

Abstract Estimating image noise levels is a critical task for many image processing applications, where the detection of homogeneous regions always plays a key role. Most conventional methods empirically divide the images into rectangular blocks and then select the most homogeneous ones. However, this approach may result in erroneous homogeneity detection, especially in the case of highly textured images. To address this challenge, a spatially cohesive superpixel model is proposed in this paper, which can decompose a noisy image into patches that adhere to local structures and hence tend to exhibit increased homogeneity. A new similarity measure is also defined, to make the superpixel model more robust to noise. Combined with histogram-based homogeneous superpixel selection and filter-based noise level calculations, our method can accurately estimate the noise level of images with various noise intensities and different image complexities. Moreover, the method is extended to the signal-dependent noise case, which is usually the case of hyperspectral images. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods.