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Unsupervised image denoising method based on uncertainty perception

An uncertain and unsupervised technology, applied in the field of image denoising, can solve the problems of poor visual effect, no consideration of accidental uncertainty denoising network, etc., and achieve the effect of reducing interference

Pending Publication Date: 2022-07-29
FUDAN UNIV
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Problems solved by technology

Although the above methods have good performance on denoising tasks, these methods tend to generate the mean of multiple possible denoising results, and do not consider the impact of accidental uncertainty in the training data set on the denoising network, resulting in network The denoising result is too smooth and the visual effect is poor

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  • Unsupervised image denoising method based on uncertainty perception
  • Unsupervised image denoising method based on uncertainty perception
  • Unsupervised image denoising method based on uncertainty perception

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Embodiment Construction

[0031] The embodiments of the present invention will be described in detail below, but the protection scope of the present invention is not limited to the examples.

[0032] The specific steps are:

[0033] (1) Before training, several smooth noise blocks with a size of 96×96 are extracted through the smooth noise block extraction rule (the number of noise blocks is closely related to the noise intensity). The original noisy image is randomly cropped into noise image blocks with a size of 96×96, and the number is 20,000, which is used as a training data set. The network model parameters are initialized by default.

[0034] (2) During training, randomly crop the 96×96 noise image and smooth the noise block to 64×64. The training period is 30, the initial learning rate is set to 0.001, and the decay rate is 0.5, which is decayed every ten epochs. Minimize the loss function using mini-batch stochastic gradient descent. The batch size is set to 128. The model adopts a staged ...

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Abstract

The invention belongs to the technical field of digital image processing, and particularly relates to an unsupervised image denoising method based on uncertainty perception. The method comprises the following steps: constructing a smooth noise extraction module and a rough denoising network to realize simulation of a noise block and rough denoising of an image; a network with modeling accidental uncertainty is constructed, pixels with high uncertainty in the noise image are estimated, an uncertainty map is generated, and the network in the next stage is guided to carry out denoising; and constructing a fine denoising network, and guiding the network to reduce the influence of uncertainty on the denoising process through an uncertainty graph weighting objective function, so that the network achieves a better denoising effect. Experimental results show that noise in the image can be effectively removed, and the denoised image has a good texture structure and visual quality.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to an image denoising method. Background technique [0002] Image denoising, as a classic and challenging image processing task, has received more and more attention. The noise in the image is mainly generated in the process of acquiring the image. Two common types of image sensors, CCD and CMOS, will introduce various noises, such as thermal noise caused by resistors, and channel heat of FETs, due to their own sensor material properties, working environment, electronic components, and circuit structures. Noise, photon noise, dark current noise, photoresponse non-uniformity noise. The presence of noise will not only degrade the visual quality of the picture, but even bring about more serious effects. For example, low-dose radiation CT images have noise due to the reduced radiation intensity, and these noises will affect the doctor's judgment on the le...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T5/10G06N3/04G06N3/08
CPCG06T5/10G06N3/08G06T2207/20064G06T2207/20081G06T2207/20084G06N3/048G06N3/045G06T5/70
Inventor 颜波谭伟敏黄辰宇
Owner FUDAN UNIV
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