Video denoising method based on actual camera noise modeling

An actual camera, noise video technology, applied in the direction of TV, color TV, computer parts, etc., can solve the problems of reduced image quality, complex noise, etc.

Active Publication Date: 2019-09-17
NANJING UNIV
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  • Abstract
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Problems solved by technology

[0002]In extremely low-light conditions, a large amount of noise can significantly degrade the image quality, so low-light video imaging is a challenging problem
A large number of video denoising or video enhancement algorithms have been proposed to solve this problem, however, most of the noise models in these algorithms are simple independent and identically distributed assumptions, including additive white Gaussian noise, Poisson noise or Gaussian noise and Poisson noise the mix of
In reality, noise in video is very complex, especially in low light conditions, and some factors that are usually ignored, such as dynamic pattern noise, noise channel connection, truncation effect, etc., will become major problems

Method used

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  • Video denoising method based on actual camera noise modeling

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Embodiment Construction

[0021] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0022] refer to figure 1 , a video denoising method for actual camera noise modeling of the present embodiment, the specific steps are as follows:

[0023] Step 1, explore the physical causes of the main noise in the imaging process and establish a mathematical model of the noise distribution. In the low-light imaging process, the high-sensitivity camera settings make some tiny noises become important noise components in low-light conditions. figure 2 The common noise sources in the whole imaging process are shown. Based on this physical imaging process, a basic noise model mainly including three noise sources is established. The whole model assumes that the camera obeys a globally consistent linear response curve with gain K. The measured value y is obtained by the following formula i :

[0024] the y i =K(S i +D i +R i ) (1)

[0...

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Abstract

The invention discloses a video denoising method based on actual camera noise modeling. The method comprises the following specific steps: (1) exploring the physical cause of main noise in an imaging process and establishing a mathematical model of noise distribution; (2) on the basis of the established noise model, carrying out model parameter calibration, and generating a noise video training set conforming to reality; (3) designing a video denoising and enhancement neural network, and combining space and time information to suppress and weaken the noise; and (4) training and optimizing the neural network, and verifying the practicability of the method by using the synthesized and actually acquired video data set. The de-noising method is suitable for de-noising weak light videos and has very important application requirements in the fields of national defense military affairs, security and protection monitoring, scientific research environment protection and the like.

Description

technical field [0001] The invention relates to the field of computational photography and deep learning, and in particular to the field of low-light video denoising based on actual camera noise modeling. Background technique [0002] Low-light video imaging is a challenging problem in extremely low-light conditions where large amounts of noise can significantly degrade image quality. A large number of video denoising or video enhancement algorithms have been proposed to solve this problem, however, most of the noise models in these algorithms are simple independent and identically distributed assumptions, including additive white Gaussian noise, Poisson noise or Gaussian noise and Poisson noise the mix of. In reality, noise in video is very complex, especially in low-light conditions, and some factors that are usually ignored, such as dynamic pattern noise, noise channel correlation, truncation effects, etc., will become major problems. [0003] Deep learning-based method...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00H04N5/21G06K9/62G06N3/04
CPCG06T5/002H04N5/21G06N3/04G06F18/214Y02T10/40
Inventor 王伟陈鑫
Owner NANJING UNIV
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