This project presented a new full color reconstruction method of noisy CFA data through an LMMSE filtering of the green-red and green-blue PDS and a wavelet-based denoising process. It is observed that the PDS is a low-pass process, and it is uncorrelated with the IE, which is a bandpass process, and the DSN, which is a low pass process but with relatively wide bandwidth. Based on these properties we estimated the PDS in both horizontal and vertical directions and then optimally fused them.
With the estimated PDS we got a full resolution green image, on which the additive noise is imposed. Considering that the additive noise is channel-dependent, we proposed a specific waveletbased denoising algorithm to remove the noise from the green channel. The resulted green channel was used to guide the reconstruction of the red and blue samples. The experiments verified that the proposed joint demosaicking-denoising color reconstruction scheme significantly suppresses the noise caused color artifacts while preserving well the image details. It outperforms the state-of-the-art color demosaicking and denoising methods.
Zhang, Lei, Xiaolin Wu, and David Zhang. “Color reproduction from noisy CFA data of single sensor digital cameras.” IEEE
Transactions on image processing 16, no. 9 (2007): 2184-2197.