Method for image processing and apparatus for implementing the same

An apparatus, the apparatus comprising a processor and a memory operatively coupled to the processor, wherein the processor is configured to perform a method of processing an image, the method comprising:

• determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (φ1, . . . , φP) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ);
• performing a convergence check loop, wherein each iteration of the convergence check loop comprises:
• generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image;
• determining an estimate (φP) of a variance of the noise template;
• if the estimated variance (φP) is below a first predetermined threshold (Tmin) or above a second predetermined threshold (Tmax), proportionally decreasing one or more of the AR model linear combination parameters (φ1, . . . , φP) with a predetermined correcting factor, and performing a new iteration of the convergence check loop;
• otherwise exiting the convergence check loop.

A non-transitory computer-readable medium encoded with executable instructions which, when executed, causes an apparatus comprising a processor operatively coupled with a memory, to perform a method of processing an image, the method comprising:

• determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (φ1, . . . , φP) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ);
• performing a convergence check loop, wherein each iteration of the convergence check loop comprises:
• generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image;
• determining an estimate (σP) of a variance of the noise template;
• if the estimated variance (σP) is below a first predetermined threshold (Tmin) or above a second predetermined threshold (Tmax), proportionally decreasing one or more of the AR model linear combination parameters (φ1, . . . , φP) with a predetermined correcting factor, and performing a new iteration of the convergence check loop;
• otherwise exiting the convergence check loop.