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Image denoising method, system and device and storage medium

An image and noise technology, applied in the field of image denoising methods, systems, equipment and storage media, can solve the problems of poor denoising effect, complex structure and details of the restored image, etc., to improve the image signal-to-noise ratio, Good subjective and objective effects, the effect of removing image noise

Pending Publication Date: 2021-05-14
CTRIP TRAVEL NETWORK TECH SHANGHAI0
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing convolutional neural network denoising methods still need to be improved in recovering the complex structure and details of the image. When the image has more texture details, the denoising effect will be worse.

Method used

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  • Image denoising method, system and device and storage medium
  • Image denoising method, system and device and storage medium
  • Image denoising method, system and device and storage medium

Examples

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Embodiment Construction

[0049]Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0050] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities m...

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Abstract

The invention provides an image denoising method, system and device and a storage medium, and the method comprises the steps: constructing an image denoising model which is used for carrying out the feature mapping of a wavelet domain and the feature mapping of a spatial domain on an input image with noise, and obtaining a denoised image; training the image denoising model; and inputting a to-be-processed image into the trained image denoising model to obtain a denoised image output by the image denoising model. The invention provides a novel image denoising model based on a double-domain network, feature extraction and mutual complementation are carried out on a spatial domain and a wavelet domain at the same time, image noise can be effectively removed, the signal-to-noise ratio of the image is improved, detail information such as textures and edges of the image can be recovered, and therefore a more ideal image denoising effect is obtained.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to an image denoising method, system, device and storage medium. Background technique [0002] In real scenes, there are many factors that affect image quality, such as data acquisition, errors introduced during transmission, and environmental interference. Image noise is the most common factor that causes image degradation. It will not only reduce the signal-to-noise ratio and resolution of the image, greatly reduce the visual experience, but also affect subsequent image analysis and understanding. [0003] Existing image denoising methods can be roughly divided into traditional model-driven denoising methods and deep learning-based denoising methods. Traditional methods include denoising methods based on partial differential equations, non-local mean denoising methods, denoising methods based on wavelet transform, methods based on sparse representation and low-ran...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T5/50G06N3/04G06N3/08
CPCG06T5/50G06N3/08G06T2207/20064G06T2207/20081G06T2207/20084G06T2207/20221G06N3/045G06T5/70
Inventor 康睿文罗超成丹妮邹宇李巍
Owner CTRIP TRAVEL NETWORK TECH SHANGHAI0
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