Abstract: A method is provided to estimate image and video noise of different types: white Gaussian (signal-independent), mixed Poissonian-Gaussian (signal-dependent), or processed (non-white). Our method also estimates the noise level function (NLF) of these noises. This is done by classification of intensity variances of image patches in order to find homogeneous regions that best represent the noise. It is assumed that the noise variance is a piecewise linear function of intensity in each intensity class. To find noise representative regions, noisy (signal-free) patches are first nominated in each intensity class. Next, clusters of connected patches are weighted where the weights are calculated based on the degree of similarity to the noise model. The highest ranked cluster defines the peak noise variance and other selected Ousters are used to approximate the NLF.

Abstract: A time-space domain video denoising method is provided which reduces video noise of different types. Noise is assumed to be real-world camera noise such as white Gaussian noise (signal-independent), mixed Poissonian-Gaussian (signal-dependent) noise, or processed (non-white) signal-dependent noise. This method comprises the following processing steps: 1) time-domain filtering on current frame using motion-compensated previous and subsequent frames; 2) restoration of possibly blurred contents due to faulty motion compensation and noise estimation; 3) spatial filtering to remove residual noise left from temporal filtering. To reduce the blocking effect, a method is applied to detect and remove blocking in the motion compensated frames. To perform time-domain filtering weighted motion-compensated frame averaging is used. To decrease the chance of blurring, two levels of reliability are used to accurately estimate the weights.

Abstract: There is described a Minimal Iterativity Anisotropic Diffusion (MIAD) approach that estimates the required number of iterations N as a function of the image structure-under-noise ? and the bound of the noise ?. The time step ? is related to the image structure-under-noise ? and to the bound of noise ?. The stopping time is calculated using T=?·N, and the edge strength ? is determined as a function of T, ?, and ?.

Abstract: There is described a Minimal Iterativity Anisotropic Diffusion (MIAD) approach that estimates the required number of iterations N as a function of the image structure-under-noise ? and the bound of the noise ?. The time step ? is related to the image structure-under-noise ? and to the bound of noise ?. The stopping time is calculated using T=?·N, and the edge strength ? is determined as a function of T, ?, and ?.

Abstract: An edge detection method employs binary morphological erosion. A binary image is generated from the gray-scale-value input image. A structure element is guided in a step-by-step manner across the binary image and generates an eroded binary image in accordance with an erosion rule. By forming the difference between the binary image and the eroded binary image, an output image containing the edges is generated. An output image which contains masked edges is generated through the use of a further erosion rule. The further erosion rule is based on a gray-scale value threshold and is applied to the eroded binary image to form a twice-eroded binary image. The difference between the twice-eroded binary image and the binary image forms the image which contains masked edges.