Image denoising method, device, equipment and storage medium

Abstract

The invention discloses an image denoising method. The method comprises steps: an average block set of a to-be-denoised image is acquired; a corresponding mean value is subtracted from each average block in the average block set to obtain a noise block set; a first noise image in the noise block set is acquired; noise modeling is carried out on the noise block set according to a generative adversarial network to obtain a generator capable of generating the same kind of noise as the to-be-denoised image; a second noise image is acquired according to the generator; according to a noise-free image, the first noise image and the second noise image, a training set is constructed; according to the training set and a discriminative learning method, an image denoising network model is trained; andthe to-be-denoised image is inputted to the image denoising network model to acquire an image after denoising. The invention also provides an image denoising device, equipment and a storage medium. the denoising effects on unknown real noise in real life can be enhanced and the denoising efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image denoising method, device, equipment and storage medium. Background technique [0002] Image is the most commonly used information carrier in today's society, but it is often disturbed and affected by various noises in the process of image acquisition, transmission or storage, which will degrade the image. Image denoising is a very important research direction in the field of image processing, and its purpose is to restore clean images from noisy images. Under different conditions, there are two scenarios for the use of denoising: one is to denoise on the premise of known noise information, which is called specific denoising. The denoising method is used for denoising, such as using median filter to remove salt and pepper noise. The other is to denoise without knowing the noise information, and this kind of removal of unknown noise is called blind denoi...

AI Technical Summary

Problems solved by technology

If Gaussian noise, which can be artificially generated, is processed under the condition of knowing the noise information, the training data is generally obtained by adding Gaussian noise to the clean image data set, but for the noise in real life, because we do not know the noise information, so the corresponding noise cannot be generated to artificially create a data set. In fact, we can only obtain the noise map by taking pictures, but not the corresponding clean picture, so it cannot constitute training data.

That is to say, for the blind denoising task, the discriminative model will be limited due to the inability to obtain training data, and cannot achieve a better denoising effect.

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