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Improved adaptive weighted average image denoising method based on extreme learning machine

An extreme learning machine and adaptive weighting technology, applied in the field of image processing and machine learning, can solve problems such as image blurring, image smoothness reduction, and high processing time complexity

Active Publication Date: 2017-06-06
ENJOYOR COMPANY LIMITED
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  • Application Information

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Problems solved by technology

This type of method will reduce the smoothness of the image to a certain extent or cause the image to become blurred
3) Partial differential equations, this type of method can better preserve image detail information, but it is not suitable for high-density noise images, and the processing time complexity is high

Method used

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  • Improved adaptive weighted average image denoising method based on extreme learning machine
  • Improved adaptive weighted average image denoising method based on extreme learning machine
  • Improved adaptive weighted average image denoising method based on extreme learning machine

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Embodiment

[0079] Example: such as figure 1 As shown, an improved adaptive weighted average image denoising method based on extreme learning machine, including the following steps:

[0080] Step 1: Input the training image set and the target image to be denoised.

[0081] Step 2: Extreme learning machine model training. The training image set is trained by the extreme learning machine to generate a noise detector. During the training of the extreme learning machine, in addition to the pixel value of the input sample, the present invention also introduces the level logic difference of the pixel, and the output sample is noise position information. The rank logic difference is defined as follows:

[0082] Suppose the pixel value of the image I pixel (x, y) is a(x, y), the (2s+1)*(2s+1) window centered on (x, y) is W, and s is a positive integer, ( x+x′, y+y′) is a pixel in the window W that is not (x, y), and the pixel value is a(x+x′, y+y′), then a(x, y) and a( The logical difference ...

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Abstract

The present invention relates to an improved adaptive weighted average image denoising method based on an extreme learning machine. The method comprises the following steps: 1) inputting a training image set and a target image to be subjected to denoising processing; 2) employing the extreme learning machine to combine the pixel value and the rank logical difference of the pixel for performing training of the training image set to obtain the extreme learning machine; 3) detecting of the target image through the extreme learning machine model, and obtaining the image noise position; 4) employing an adaptive weighted average algorithm for image denoising processing; and 5) outputting the target image after the denoising processing, and performing assessment of the denoising effect. The improved adaptive weighted average image denoising method based on the extreme learning machine is high in applicability, high in feasibility, fast in calculation speed, high in effectiveness and high in practical value and can reach good image denoising effect.

Description

technical field [0001] The invention relates to the fields of image processing and machine learning, in particular to an improved adaptive weighted average image denoising method based on an extreme learning machine. Background technique [0002] With the popularity of various digital instruments and digital products, digital images have become one of the most commonly used information carriers in human life, and are widely used in transportation, medical, aerospace, maritime and other fields. In the process of formation, transmission, storage and conversion of digital images, it is inevitable to be affected by various noises, resulting in the degradation of image quality. Image denoising refers to improving image quality, eliminating or reducing the influence of noise in images, increasing image signal-to-noise ratio, and preserving image integrity. As an important link and key step in digital image processing, the quality of image denoising results directly affects subseq...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20081G06T2207/20008G06T5/70
Inventor 李丹吴越李建元钱智刚于海龙刘兴田刘飞黄刘祥
Owner ENJOYOR COMPANY LIMITED
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