Regional aware image de-noising method based on machine learning

A machine learning and area-aware technology, applied in the field of machine-learning-based area-aware image denoising, can solve problems such as not being applicable to all areas of the image

Inactive Publication Date: 2018-08-17
FUZHOU UNIV
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Problems solved by technology

Currently existing image denoising algorithms use the same parameters to denoise the e

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  • Regional aware image de-noising method based on machine learning
  • Regional aware image de-noising method based on machine learning
  • Regional aware image de-noising method based on machine learning

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Embodiment Construction

[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] The present invention provides a machine learning-based region-aware image denoising method, such as figure 1 shown, including the following steps:

[0047] Step S1: For the noise images in the training set under different noise amplitudes, use the noise-added standard deviation σ and the reduced standard deviation r after k reduction ratios j ×σ are respectively used as denoising parameters, j=1, 2,...,k, and denoising result sets with different denoising parameters are obtained.

[0048] In this embodiment, noise-added standard deviation σ and seven reduction ratios R={0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95} reduced standard deviation r are used for noise images under different noise amplitudes j ×σ are respectively used as denoising parameters, where r j ∈R, the denoising result set with different denoising parameters is o...

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Abstract

The invention relates to a regional aware image de-noising method based on machine learning, comprising the following steps: 1. using a noise standard deviation [sigma] and a standard deviation rj*[sigma] reduced by k-type reduction ratio as noise de-noising parameters to obtain different de-noising result sets; 2, combining the [sigma] with the de-noising results using rj*[sigma] to obtain the preference of the optimal reduction ratio r<~> and an image block using the two de-noising parameters using [sigma] and r<~>*[sigma]; 3, performing feature extraction on the noise image and the de-noising results using the two de-noising parameters; 4, using an obtained preference feature set as a feature set of a machine learning algorithm to learn the de-noising parameter preference model of the image block; 5, using the de-noising parameter preference model to predict the noise image in the test set to obtain the predicted preference probability value of each image block; and 6, performing threshold processing and combining the de-noising results of the two de-noising parameters to obtain a final de-noising result. The method can improve the performance.

Description

technical field [0001] The invention relates to the fields of image and video processing and computer vision, in particular to an area-aware image denoising method based on machine learning. Background technique [0002] Humans rely on their senses to receive information from the outside world. Stepping into the era of digital images, image information is one of the types of information that human beings are most exposed to. Images may be disturbed by various noise signals during the process of acquisition, transmission, and storage, such as sensor noise caused by low brightness, and failed pixels in the camera sensor. Image denoising is an important topic in digital image processing. Noisy images are ubiquitous in practical applications. Noise not only interferes with human vision, but also seriously affects the performance of image analysis, image understanding and image processing algorithms, such as: image segmentation, image saliency detection, image recognition, ima...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/002G06T2207/10004G06T2207/20021G06T2207/20076
Inventor 牛玉贞林乐凝陈羽中杨彦
Owner FUZHOU UNIV
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