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Image denoising method based on visual non-classical receptive field model

A non-classical receptive field and visual technology, applied in the field of image processing, can solve the problem of the decline of the response curve, and achieve the effect of retaining details

Inactive Publication Date: 2011-10-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, a larger area of ​​stimulation leads to a sharp decline in the response curve, which is caused by the inhibition caused by the stimulation area exceeding the central area and reaching the peripheral inhibition area.

Method used

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  • Image denoising method based on visual non-classical receptive field model
  • Image denoising method based on visual non-classical receptive field model
  • Image denoising method based on visual non-classical receptive field model

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Embodiment approach 1

[0048] Embodiment 1—Processing the simulated ladder image with Gaussian noise added

[0049] Figure 5 (a) is the original ladder image, Figure 5 (b) is a ladder image with Gaussian white noise (σ=10), and the right image is the pixel grayscale image of the horizontal center line of the image. We set the model parameters as d c = 3, d r = 10, d u = 3, A c = 1.0, A r =1,0,A u =1.0, then the image processed according to the detailed technical scheme of the present invention is shown in Figure 5 (c), it can be seen that due to the expansion of the receptive field size in the low-contrast area, the high-frequency noise is significantly reduced. At the same time, in the high-frequency area such as the edge of the step, the edge is preserved and enhanced due to the contraction of the receptive field caused by high contrast (Mach effect ).

specific Embodiment approach 2

[0050] Specific implementation mode two - filtering natural images with Gaussian noise, salt and pepper noise and taint noise added

[0051] The present invention is used to denoise a group of natural images added with Gaussian self-noise, salt-and-pepper noise and stain noise, wherein the noise intensity is σ=30. The diameter of the receptive field is the parameter corresponding to the data in bold in Table 1 to Table 3, and the sensitivity of the central area, peripheral area and sub-area of ​​the receptive field is 1.0. Tables 1 to 3 show the peak-to-peak signal-to-noise ratios of Gaussian white noise, salt and pepper noise, and stain noise before and after filtering of the output image processed according to the detailed technical solution of the present invention. PSNR1 represents the peak-to-peak signal-to-noise ratio of the polluted image, and PSNR2 represents the peak-to-peak signal-to-noise ratio of the filtered image is given in the table header. It can be seen from...

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Abstract

The invention discloses an image denoising method based on a visual non-classical receptive field model, belonging to the technical field of image processing, and relating to an image denoising method. Based on the newest optic nerve electrophysiology research result, the invention simulates the retina visual mechanism of human eyes, builds a visual non-classical receptive field model and realizes the image denoising processing. The invention not only can remove common noise in the image processing process and has the function of strengthening image edge details. Mass biology experiment result stimulation and practical image denoising experiments prove the accuracy and the effectiveness of the invention.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image noise removal method, in particular to an image denoising method based on a visual receptive field model. Background technique [0002] As early as 1953, Kuffler et al. found that the receptive field of retinal ganglion cells and lateral genuform soma cells consisted of two regions: a small peripheral region and a slightly larger ring-shaped peripheral region. The peripheral region’s influence on cell activity is antagonistic. For example, for a receptive field with a central 'On' type, when the bright side with contrasting edge illuminates the central area and the dark edge side illuminates the peripheral area, it will cause maximum excitation in the central area and maximum inhibition in the peripheral area. Cells that are some distance from the edge are only slightly affected. The image processing effect produced by the interaction between retinal ganglion ce...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06N3/02
Inventor 杨莉杜馨瑜尧德中李朝义
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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