Denoising camera data: Shape-adaptive noise reduction for color filter array image data

While denoising readily processed images has been studied extensively, the reduction of camera noise in the camera raw data is still a challenging problem. Camera noise is signal-dependent and the raw data is a color filter array (CFA) image, which means the neighboring values are not of the same color and standard denoising methods cannot be used. In this paper, we propose a new method for efficient raw data denoising that is based on a shape-adaptive DCT (SA-DCT), which was originally proposed for non-CFA data. Our method consists of three steps: a luminance transformation of the Bayer data, determining an adequate neighborhood for denoising and hard thresholding in the SA-DCT domain. The SA-DCT is applied on realistic CFA data and accounts for the signal-dependent noise characteristic using a locally adaptive threshold and signal-dependent weights. We additionally present a computationally efficient solution to suppress flickering in video data. We evaluate the method quantitatively and visually using both realistically simulated test sequences and real camera data. Our method is compared to the state-of-the-art methods and achieves similar performance in terms of PSNR. In terms of visual quality, our method can reach more pleasant results compared to state-of-the-art methods, while the computational complexity is kept low.