Adaptive noise intensity video denoising method and system thereof

Abstract

The invention discloses an adaptive noise intensity video denoising method which is based on motion detection and is embedded in an encoder. The method comprises the following steps: (1) taking a sum of regularization frame differences in a neighborhood as an observed value, dividing input pixels into a static pixel and a dynamic pixel and using filters in different supporting domains for the two kinds of the pixels, wherein a filtering coefficient is adaptively determined according to noise intensity and an image local characteristic; (2) taking a single DCT coefficient or the sum of the several DCT coefficients as the characteristic, using AdaBoost as a tool to construct a cascade-form classifier and using the classifier to select a static block; (3) establishing a function model of connection between DCT coefficient distribution parameters of the video noise intensity and the static block and using the model to estimate the noise signal standard difference. By using noise intensity estimation embedded in the video encoder and a noise reduction technology provided in the invention, few computation costs can be used to acquire the parameters and the information needed by noise filtering. A time efficiency is good. Because a reliable clue is used to determine whether the pixels accord with a static hypothesis, the filter of the invention can effectively filter the noise and simultaneously maintain marginal sharpness of the static image. And motion blur caused by filtering in a motion area can be avoided.

Description

technical field [0001] The invention relates to the field of video image processing, in particular to a method for suppressing video image noise that can be embedded in a video encoder and has self-adaptive noise intensity. Background technique [0002] Video surveillance systems require cameras to continuously capture video images. During the acquisition of video images, due to the defects of imaging equipment or some unpredictable factors in the imaging process, various types of noise will inevitably be introduced. The existence of noise will not only reduce the image quality in the visual sense, but more importantly, it will affect the subsequent processing. [0003] The video signal obtained by imaging devices such as CCD and CMOS camera can be modeled as an ideal video superimposed with a noise signal, namely: I k (x, y) = S k (x,y)+η k (x, y), where S k (x, y) is an ideal video signal, η k (x, y) is the noise term, usually assumed to be independent of the signal,...

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Problems solved by technology

Obviously, this estimation method cannot use the information generated by the encoder, and needs to e

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