Image denoising method and system based on neural network

Abstract

The invention discloses an image denoising method based on a neural network. The image denoising method based on the neural network includes the steps of acquiring high-sensitivity images and low-sensitivity images of a scene, generating a first neural network model, denoising the low-sensitivity images via the first neural network model to obtain denoised low-sensitivity images, constituting training image database through the high-sensitivity images and the denoised low-sensitivity images and using the training image database to train a neural network model so as to generate a second neuralnetwork model which is used for image denoising, using images to be processed as input to the second neural network model and then acquiring corresponding clean images after treatment. Furthermore, aimage denoising system based on the neural network is also disclosed. The above-mentioned image denoising method and system based on the neural network have the advantages of being capble of processing images with real noise information and having practical application value.

Description

technical field [0001] The invention relates to the fields of mathematical image processing and pattern recognition, in particular to an image denoising method and system based on a neural network. Background technique [0002] Use the method of pattern recognition to filter the noise image to obtain the noise corresponding to the noise image, and then use the image pixel correspondence to separate the corresponding noise from the noise image to obtain the corresponding clean image. [0003] In traditional image denoising processing, pattern recognition methods including CNN (Convolution Neural Network Convolutional Neural Network) have important applications, but the training image databases used to construct these convolutional neural networks are mostly images containing artificial noise database, so it can only deal with artificially synthesized noise, and has little effect on real noisy images. [0004] Considering that the noise in real life is polygonal, rather than ...

AI Technical Summary

Problems solved by technology

Although the convolutional neural network constructed using the above training image database can process the actual noise during image denoising processing, in practical applications, the clean image obtained after processing still does not have high-quality characteristics.

It can be seen that the image denoising method in the prior art cannot achieve a good denoising effect

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