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A CNN Denoising Method Based on Parallel Feature Extraction

A feature extraction and noise technology, applied in the field of image denoising, can solve problems such as unsatisfactory denoising effect

Active Publication Date: 2019-12-31
ANHUI UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the overall denoising effect obtained by the existing methods is not satisfactory.

Method used

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  • A CNN Denoising Method Based on Parallel Feature Extraction
  • A CNN Denoising Method Based on Parallel Feature Extraction
  • A CNN Denoising Method Based on Parallel Feature Extraction

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Embodiment Construction

[0048]The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0049] Such as figure 2 As shown, the present invention discloses a CNN denoising method based on parallel feature extraction, including six steps. Step S1, build a CNN denoising network model for parallel feature extraction; Step S2, initialize the training parameters of the CNN denoising network model; Step S3, build a training set; Step S4, design a loss function, and aim to minimize the loss function Train the CNN denoising network model to obtain the CNN...

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Abstract

The invention discloses a CNN denoising method based on parallel feature extraction, comprising six steps: Step 1, building a CNN denoising network model for parallel feature extraction; Step 2, initializing the training parameters of the CNN denoising network model; Step 3, constructing Training set; step 4, design the loss function, and train the CNN denoising network model with the goal of minimizing the loss function to obtain the CNN denoising model; step 5, use the noise image as the input of the CNN denoising model, and its output is The noise information learned by the network model; Step 6, subtract the noise information learned in Step 5 from the noisy image to obtain a clean image after denoising. The invention can thoroughly remove the noise, well preserve the texture information of the image, and significantly improve the objective indexes PSNR and SSIM.

Description

technical field [0001] The invention relates to the field of image denoising, in particular to a CNN denoising method based on parallel feature extraction. Background technique [0002] In reality, digital images are often affected by imaging equipment and external environmental noise interference during digitization and transmission, which are called noisy images or noisy images. The ultimate goal of image denoising is to improve a given image and solve the problem of image quality degradation caused by noise interference in actual images. Denoising technology can effectively improve the image quality, increase the signal-to-noise ratio, and better reflect the information carried by the original image. As an important preprocessing method, people have conducted extensive research on image denoising algorithms. [0003] At present, there are many classic methods for image denoising, but they can be roughly divided into two categories, one is based on spatial domain filterin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06N3/04
CPCG06T5/002G06T2207/20081G06T2207/20084G06N3/045
Inventor 赵佰亭贾晓芬郭永存黄友锐柴华荣
Owner ANHUI UNIV OF SCI & TECH
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