The noise in digital images acquired by image sensors has complex characteristics due to the variety of noise sources. However, most noise reduction methods assume that an image has additive white Gaussian noise (AWGN) with a constant standard deviation, and thus such methods are not effective for use with image signal processors (ISPs). To efficiently reduce the noise in an ISP, we estimate a unified noise model for an image sensor that can handle shot noise, dark-current noise, and fixed-pattern noise (FPN) together, and then we adaptively reduce the image noise using an adaptive Smallest Univalue Segment Assimilating Nucleus ( SUSAN ) filter based on the unified noise model. Since our noise model is affected only by image sensor gain, the parameters for our noise model do not need to be re-configured depending on the contents of image. Therefore, the proposed noise model is suitable for use in an ISP. Our experimental results indicate that the proposed method reduces image sensor noise efficiently.

This paper reports on the suitability of the SUSAN filter for the removal of artifacts that result from quantization errors in wavelet video coding. In this paper two extensions of the original filter are described. The first uses a combination of 2-D spatial filtering followed by 1-D temporal filtering along motion trajectories, while the second extension is a pure 3-D motion compensated SUSAN filter. The SUSAN approach effectively reduces coding artifacts, while preserving the original signal structure, by relying on a simple pixel-difference-based classification procedure. Results reported in the paper clearly indicate that both extensions efficiently reduce ringing that is the prevalent artifact perceived in wavelet-based coded video. Experimental results indicate an increase in perceptual as well as objective (PSNR) decoded video quality, which is competitive with state-of-the-art post-processing algorithms, especially when low computational demands of the proposed approach are taken into account.