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Machine learning-based noise image saliency detecting method

A technology of machine learning and detection methods, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as compression distortion, jitter and distorted images, and achieve the effect of improving detection performance

Inactive Publication Date: 2016-08-31
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

[0005] Most of the images in real life are distorted, such as the distortion caused by the camera sensor, image processor and other peripherals, the shaking distortion caused by the shaking of the camera equipment, and the image compression distortion, etc.

Method used

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  • Machine learning-based noise image saliency detecting method

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Embodiment Construction

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

[0046] The present invention provides a noise image saliency detection method based on machine learning, such as figure 1 and image 3 shown, including the following steps:

[0047] Step S1: Perform denoising processing on the noise image of each amplitude using various denoising parameters to obtain the optimal denoising parameter corresponding to each amplitude. In this embodiment, step S1 specifically includes the following steps:

[0048] Step S11: Use 9 Gaussian low-pass filtering denoising parameters (template sizes are {3×3, 5×5, 7×7}, standard deviations are {0.5, 0.7, 0.9}) to the noise of each amplitude The image is denoised to obtain a denoised image set S containing 9 denoising parameters for each amplitude;

[0049] Step S12 : using the saliency detection method VA (Saliency detection via absorbing markov chain) to calc...

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Abstract

The invention relates to a machine learning-based noise image saliency detecting method which comprises the following steps: 1, a plurality kinds of denoising parameters are adopted for a noise image of each amplitude, and an optimal denoising parameter for each amplitude is obtained; 2, each noise image is subjected to characteristic extracting operation via a noise assessing algorithm, noise value characteristics are obtained, and a noise value characteristic set is formed; 3, the noise value characteristic set is used as a machine learning algorithm characteristic set, and a noise amplitude prediction model is obtained via a machine learning algorithm and a quinquesection cross validation method; 4, a noise image corresponding to the noise amplitude prediction model is adopted for prediction, and predicted noise amplitude value is obtained; 5, predicted noise amplitude value of each noise image and a corresponding optimal denoising parameter are used for denoising operation, and a denoised image set can be obtained; 6, images in the denoised image set is subjected to saliency detecting operation via a saliency detection method, and a final salient image can be obtained. According to the machine learning-based noise image saliency detecting method, noise image detecting performance can be improved.

Description

technical field [0001] The invention relates to the technical fields of image and video processing and computer vision, in particular to a noise image saliency detection method based on machine learning. Background technique [0002] Human senses mainly include vision, smell, taste, hearing and touch. Humans rely on their senses to receive information from the outside world. Vision plays an important role in human senses. The human visual system can focus on the most important part of the image in a short time, that is, the part that the human eye is most interested in. With the advent of the multimedia era, the popularization of various digital products and the dissemination of digital images in the Internet age, a large number of image resources are generated and transmitted every day. Although massive image data enriches life, it also brings many challenges. [0003] How to efficiently and accurately process these image resources is a key issue. After discovering the...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/00
CPCG06T7/0002G06T2207/20081G06T2207/20028G06T5/70
Inventor 牛玉贞林乐凝陈羽中
Owner FUZHOU UNIV
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