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Image noise reduction model generation method, image noise reduction method and device, storage medium and equipment

An image noise reduction and image technology, which is applied in the field of image processing and can solve problems such as poor versatility and loss of clarity.

Pending Publication Date: 2021-02-19
SHENZHEN ARASHI VISION CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide an image noise reduction model generation method, image noise reduction method, device, storage medium and equipment, which are used to solve the problems of poor versatility and loss of definition in existing Raw image noise reduction methods

Method used

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  • Image noise reduction model generation method, image noise reduction method and device, storage medium and equipment
  • Image noise reduction model generation method, image noise reduction method and device, storage medium and equipment
  • Image noise reduction model generation method, image noise reduction method and device, storage medium and equipment

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Embodiment 1

[0064] figure 1 The implementation flow of a method for generating an image noise reduction model provided by Embodiment 1 of the present invention is shown. The method for generating an image noise reduction model provided by the embodiment of the present invention can be applied to a computing device, where the computing device can be but not limited to various personal computers, notebook computers, smart phones, tablet computers, and panoramic cameras.

[0065] For illustrative purposes, figure 1 Only the parts related to the embodiments of the present invention are shown, and the details are as follows:

[0066] Such as figure 1 As shown, the image noise reduction model generation method includes the following steps:

[0067] S101. Acquire the first Raw images of N different Bayer arrays in the same scene;

[0068] Wherein, the first Raw image may be captured by any device with a shooting function, such as a digital camera, a panoramic camera, a mobile phone, a tablet...

Embodiment 2

[0104] figure 2 The implementation flow of an image noise reduction method provided by Embodiment 2 of the present invention is shown. The image noise reduction method provided by the embodiments of the present invention can be applied to computing devices, where the computing devices can be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and panoramic cameras.

[0105] For illustrative purposes, figure 2 Only the parts related to the embodiments of the present invention are shown, and the details are as follows:

[0106] Such as figure 2 As shown, the embodiment of the present invention provides an image noise reduction method, the method includes the following steps:

[0107] S201: Acquire a Raw image to be processed;

[0108] In an embodiment, the Raw image to be processed can be captured by any device with a shooting function, such as a digital camera, a panoramic camera, a mobile phone, a tablet computer, an acti...

Embodiment 3

[0128] image 3 The structure of the image noise reduction model generation device 3 provided by the third embodiment of the present invention is shown, and for the convenience of description, only the parts related to the embodiment of the present invention are shown.

[0129] In the embodiment of the present invention, the image noise reduction model generation device 3 includes an acquisition module 31, a first processing module 32, a second processing module 33 and a training module 34, wherein:

[0130] Obtaining module 31: used to obtain the first Raw images of N different Bayer arrays in the same scene, where N is an integer greater than or equal to 2;

[0131] The first processing module 32: for performing fusion processing on the first Raw images of the N different Bayer arrays respectively, to obtain corresponding N second Raw images;

[0132] The second processing module 33: for establishing a sample image database based on the first Raw image and the second Raw im...

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Abstract

The invention provides an image noise reduction model generation method and an image noise reduction method, and the method comprises the steps: obtaining N first Raw images of different Bayer arraysin the same scene, carrying out the fusion processing of the N first Raw images of different Bayer arrays, obtaining N corresponding second Raw images, building a sample image database based on the first Raw images and the second Raw images, and carrying out the model training, obtaining an image noise reduction model; and performing format conversion processing on the Raw image to be processed, inputting the processed Raw image into the image noise reduction model to obtain a first noise reduction image, and performing corresponding format conversion processing on the first noise reduction image to obtain a final noise reduction image. Automatic image noise reduction is realized based on the image noise reduction model generated by the method, image noise can be obviously reduced, and theimage noise reduction method is high in efficiency, good in universality and high in robustness.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image noise reduction model generation method, image noise reduction method, device, storage medium and equipment. Background technique [0002] Whether it is a CCD or a CMOS chip, high temperatures will inevitably be generated during work. If the temperature of the chip rises and the noise signal is too strong, variegated spots with different brightness will be formed on the screen, especially in dark areas. These spots are the noises mentioned above. The thermal noise is generated because the thermal current is superimposed on the normal signal current, so the signal current of some pixels is greater than the normal induction current, which eventually leads to a stronger signal intensity on this pixel point, which is reflected in the Raw image with higher brightness. . After such pixels are processed by the back-end ISP, it will cause noise problems su...

Claims

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Application Information

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IPC IPC(8): G06K9/40G06K9/62G06N20/00
CPCG06N20/00G06V10/30G06F18/25G06F18/214
Inventor 吕朋伟姜文杰
Owner SHENZHEN ARASHI VISION CO LTD
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