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Image denoising and image coding and decoding method and device including image denoising

An image coding and image technology, applied in the field of image processing, can solve the problems of storage resource consumption and high burden of denoising processing speed, so as to ensure image quality and look and feel, reduce the burden of storage resources and processing speed, and reduce noise. Effect

Active Publication Date: 2022-04-15
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This brings a large burden on storage resource consumption and denoising processing speed

Method used

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  • Image denoising and image coding and decoding method and device including image denoising
  • Image denoising and image coding and decoding method and device including image denoising
  • Image denoising and image coding and decoding method and device including image denoising

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] This example provides a neural network-based video denoising method, including:

[0064] The pixel values ​​of the image area to be denoised and the reconstruction prediction residual value of the image area to be denoised are input to the convolutional neural network. The specific structure of the neural network is as figure 2 shown. Use the pixel value processing subnetwork to process the pixel values ​​of the image region to be denoised, and use the prediction residual value processing subnetwork to process the reconstruction prediction residual value of the image region to be denoised; use a mixed processing subnetwork Jointly process the output of the pixel value processing sub-network and the output of the prediction residual value processing sub-network, and each layer of the neural network of the mixed processing sub-network processes the input of the mixed processing sub-network or the output of the upper layer of the neural network of the sub-network. The o...

Embodiment 2

[0068] This example provides a neural network-based video denoising method, including:

[0069] The pixel values ​​of the image area to be denoised and the reconstruction prediction residual value of the image area to be denoised are input to the convolutional neural network. The specific structure of the neural network is as image 3 shown. Use the pixel value processing subnetwork to process the pixel values ​​of the image region to be denoised, and use the prediction residual value processing subnetwork to process the reconstruction prediction residual value of the image region to be denoised; use a mixed processing subnetwork Jointly process the output of the pixel value processing sub-network and the output of the prediction residual value processing sub-network, and each layer of the neural network of the mixed processing sub-network processes the input of the mixed processing sub-network or the output of the upper layer of the neural network of the sub-network. The ou...

Embodiment 3

[0073] This example provides a neural network-based video denoising method, including:

[0074] The pixel values ​​of the image area to be denoised and the reconstruction prediction residual value of the image area to be denoised are input to the convolutional neural network. The specific structure of the neural network is as Figure 4 shown. Use the pixel value processing subnetwork to process the pixel values ​​of the image region to be denoised, and use the prediction residual value processing subnetwork to process the reconstruction prediction residual value of the image region to be denoised; use a mixed processing subnetwork Jointly process the output of the pixel value processing sub-network and the output of the prediction residual value processing sub-network, and each layer of the neural network of the mixed processing sub-network processes the input of the mixed processing sub-network or the output of the upper layer of the neural network of the sub-network. The o...

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Abstract

The invention provides an image denoising and an image codec method and device including image denoising. The image denoising method and device adopts a convolutional neural network model; the input of the neural network is the pixel value of the image area to be denoised and the reconstruction prediction residual value of the image area to be denoised. The image encoding or decoding method and device including image denoising adopts a convolutional neural network model; the input of the neural network includes reconstructed prediction residuals and reconstructed pixel values ​​or reconstructed pixel values ​​after filtering. The present invention can learn the mapping relationship between noisy image pixel values ​​and noise-free image pixel values ​​with a smaller network scale by increasing the reconstruction prediction residual input, thereby denoising the noisy image pixel values, while ensuring image subjective quality. The invention can be applied to user-oriented image playback and image preprocessing in various computer vision fields.

Description

technical field [0001] The present invention relates to image processing technology, in particular to an image denoising method and device based on neural network and an image encoding or decoding method and device including image denoising. Background technique [0002] Image denoising is a classic problem in the field of image processing. The subjective quality of noisy images is poor, which affects people's perception. At the same time, noisy video will also affect the performance of other computer vision algorithms. Therefore, it is essential to filter the image. [0003] Existing filtering techniques include spatial domain filtering, transform domain filtering, dictionary learning, and deep learning. [0004] Spatial filtering directly processes the image pixel values ​​to be filtered. [0005] Transform domain filtering transforms the pixel values ​​of the image to be filtered to the transform domain, filters on the transform domain and then inversely transforms ba...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T9/00
CPCG06T5/002G06T9/002
Inventor 虞露李道文
Owner ZHEJIANG UNIV
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