GAN (Generative Adversarial Nets)-based CFA (Color Filer Array) image demosaicing joint denoising method

A technology of demosaicing and joint denoising, applied in the field of image processing, can solve the problems of loss of detail information, poor robustness to noise variance, image artifacts, etc., to avoid unnatural colors, enrich detailed information, and improve visual effects.

Inactive Publication Date: 2018-09-04
XIDIAN UNIV
View PDF3 Cites 32 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The shortcomings of this method are: there are many areas containing unnatural colors, and the noise removal is not clean. More importantly, the noise model learned by this method from a large amount of training data is tailored for a single noise level. Therefore, the algorithm is less robust to noise variance
However, the shortcomings of this method are: the shallow convoluti

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • GAN (Generative Adversarial Nets)-based CFA (Color Filer Array) image demosaicing joint denoising method
  • GAN (Generative Adversarial Nets)-based CFA (Color Filer Array) image demosaicing joint denoising method
  • GAN (Generative Adversarial Nets)-based CFA (Color Filer Array) image demosaicing joint denoising method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0042] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0043] Refer to attached figure 1 , further describe in detail the steps realized by the present invention.

[0044] Step 1, obtain the training sample set.

[0045] Randomly find 1400 color images from the database as the output training sample set, use a filter to downsample each color image to obtain a downsampled image, and combine all downsampled images to form a downsampled image set.

[0046] Use the Gaussian random noise method to add noise to each image in the down-sampled image set to obtain a noisy color filter array CFA image, and use all noisy color filter array CFA images to form an input training sample set .

[0047] The steps of the noise adding method of the Gaussian random noise are as follows:

[0048] In the first step, in the range of [0,20], construct a random number matrix equal to the dimension of the downsampled image.

[0049]...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a GAN (Generative Adversarial Nets)-based CFA (Color Filer Array) image demosaicing joint denoising method. The method comprises the following steps: (1) a training sample setis acquired; (2) the GAN is built; (3) parameters of a 9-layer convolution neural network are updated; (4) parameters of a 39-layer convolution neural network are updated; (5) whether the updating times of the 39-layer convolution neural network and the 9-layer convolution neural network reach 200 times is judged, if yes, a step (6) is executed, or otherwise, the step (3) is executed; (6) a nonlinear mapping relationship is built; and (7) an image after demosaicing and denoising is acquired. The color information of a noisy CFA image obtained by a digital camera can be well recovered, the noise introduced during an image acquisition process of the digital camera can be effectively suppressed, the appearance of unnatural colors is reduced, and the visual effects of a color image are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a color filter array CFA (ColorFiler Array) image demosaic joint denoising method based on Generative Adversarial Nets (GAN) in the technical field of digital camera image restoration. The present invention can be used to restore the complete color information of the noise-containing color filter array CFA image in a single-sensing camera, thereby solving the problems of loss of image color information and introduction of noise caused by reducing the cost of camera hardware; at the same time, it can also be used for The original data saved by the camera in "Raw Mode" is processed in the computer to obtain high-quality color images. Background technique [0002] Single-sensing cameras use a single CCD (Charge Coupled Deyice) or CMOS (Complementary Metal Oxide Semiconductor) sensor chip to capture images by covering a color filter array CFA in front of the single sen...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T5/00G06T3/40
CPCG06T3/4015G06T5/002G06T2207/10004G06T2207/10024G06T2207/20081G06T2207/20084
Inventor 董伟生袁明石光明谢雪梅吴金建
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap