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Image noise level estimation method based on deep learning

An image noise and deep learning technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as inapplicability, and achieve the effect of accurate estimation, good estimation and strong robustness.

Inactive Publication Date: 2018-05-04
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0003] Common noise estimation algorithms are designed for Gaussian white noise, which is not suitable for this signal-dependent noise

Method used

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  • Image noise level estimation method based on deep learning
  • Image noise level estimation method based on deep learning
  • Image noise level estimation method based on deep learning

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Embodiment Construction

[0037] The technical scheme adopted in the present invention is:

[0038] Step 1. Propose a signal-dependent noise (SDN) model:

[0039] I=f(L I )

[0040] I N =f(L I +n s +n c )+n q

[0041] Among them, I represents an ideal noise-free image, and I N Indicates the noise image actually obtained by the CCD camera, f( ) indicates the camera response function, Indicates that it depends on the light intensity L I noise component, Indicates the noise component that has nothing to do with the signal, n q Indicates the quantization noise. This component can be ignored due to the low intensity of quantization noise compared to other noises. n here s and n c The noise parameter of is assumed to be

[0042] Step 2. Perform data preprocessing

[0043] Step 2.1, obtain the BSD500 (Berkeley Image Segmentation) data set and download the noise-free image on the Internet as the original noise-free image set.

[0044] Step 2.2. Use the noise model proposed in step 1 to ar...

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Abstract

The invention belongs to the field of computer image processing and aims to provide a framework based on deep learning to effectively estimate an image noise level. For this purpose, a technical scheme is adopted. According to the technical scheme, an image noise level estimation method based on deep learning comprises the steps that 1, a signal dependent noise (SDN) model is proposed; 2, data preprocessing is performed; 3, a neural network structure is constructed; 4, the learning rate and momentum parameters of a network are set, a deep learning framework caffe is utilized to train the convolutional neural network till cost loss is reduced to a certain degree and training reaches the maximum number of iterations, a training model is generated, and a cost loss function is selected; and 5,an image with noise is input into the trained model, and a noise level function is output. The method is mainly applied to computer image processing.

Description

technical field [0001] The invention belongs to the field of computer image processing, and relates to the estimation of the noise level of a charge-coupled device and a convolutional neural network. Specifically, the convolutional neural network is used to extract image features, learn the signal-related noise distribution, and establish the mapping relationship between the end-to-end noise image and the corresponding noise level function. Background technique [0002] Charge-coupled Device (CCD), as the core device of a digital camera, will generate complex noise during the imaging process. This noise is not simply additive Gaussian White Noise (AWGN), but a signal-dependent noise (SDN) that depends heavily on signal strength. Knowing the noise level of CCD (Charge Coupled Device) can not only be used to evaluate the quality of the image sensor, but also can be used to adjust the parameters of many computer vision algorithms, so it is of great significance to accurately e...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20081G06T2207/20084G06T5/70
Inventor 杨敬钰刘鑫宋晓林李坤
Owner TIANJIN UNIV
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