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.