The spatial correlation problem of noise in imaging deblurring and its solution

We describe the spatial correlation problem of noise in colour digital images and analyse its cause. Pixel-correlated image processing procedures, such as CFA colour interpolation and colour space transformation, mainly lead to this problem. Considering this problem, we propose a new noise model based on a joint Gaussian probability distribution. Furthermore, we present an algorithm that makes the revised noise model fit the existing image deconvolution well. The parameters of our algorithm depend only on the image processing procedures of the imaging system. Finally, we apply the proposed algorithm to revise two typical image deconvolution methods and perform simulations and real-world experiments. Both the quantitative indicators and visual performance of the image deblurring results show that the revised deconvolution methods based on our noise model behave better in reducing the noise and ringing artefacts, thus improving the image quality compared with the methods that use the original noise model.