A PDE-Based Noise Suppression Method of Contaminated Remote Sensing Images Generated by Increasing CCD Integration Time

After operating on orbit for certain periods, natural degradation of CCD will result in the issue that the image SNR begins to apparently decreasing. To increase image SNR, one of the feasible ways is to increase CCD integration time. However, more Gaussian-like thermal noise, which could blur the image and even decrease the compression ratio of onboard lossless compression, will be added into the image afterwards. Therefore, it is critical to adequately consider a way to mitigate this problem. PDE-based anisotropic diffusion has been used for a range of fields in image processing such as feature extraction and edge enhancement. Nevertheless, determination of gradient threshold K value in conventional methods still remains to be uncertain. Particularly, for noise suppression of the image contaminated by exposure-induced Gaussian noise, yet, there is no method proposed to estimate more accurate K value to make the restored image have similar quality as the one acquired before increasing integration time. For this reason, UK-DMC multispectral images are selected as the test data and a new thought is proposed in this paper to estimate K value based on estimation of local noise variance and root-finding algorithm. It is finally indicated that proposed estimation method can provide a more adaptive way to determine K value for anisotropic diffusion.