Edge Extraction of Infrared Images

Abstract: An edge extraction algorithm based on the micro-motion mechanism of human eyes is proposed. By simulating the micro-motion of the eyeball, the micro-motion edge of the image is extracted. At the same time, in order to reduce the generation of pseudo edges, the micro-motion edge image is processed by mean filtering. Finally, non-maximum suppression and double threshold edge detection are applied to extract the binary edge of the image. The experimental results show that the edge extraction effect of the algorithm is good and the expected effect is achieved.

Most traditional algorithms are based on differential gradient operators of local windows, which are sensitive to noise and therefore not suitable for processing images that are severely disturbed by noise. Since human eye micromotion has the characteristics of super-resolution, it provides a good inspiration for infrared image processing. This paper studies the extraction of infrared image edges based on the mechanism of human eye micromotion and verifies it through experiments. The experimental results show that the infrared image edge extraction algorithm based on the mechanism of human eye micromotion can not only accurately extract the edge of the image, but also can well reduce the pseudo-edges in the image, and has a good edge extraction effect.

1 Principle of micro-motion imaging of human eyes

As early as 1952, Ditchburn, Ginsborg and others noticed that the human eye has unconscious tiny movements in the fixation state, namely human eye micromotion [5,6], which can be divided into three modes: high-frequency vibration, drifting movement and flickering. Since the amplitude of the first two is not large and the current eye detection technology is difficult to measure accurately, the human eye micromotion mentioned here mainly refers to flickering. Foreign scholars have conducted a large number of experimental studies on eye micromotion. The experiment shows that when the human eye stops all eye movements, the static image will suddenly become blurred and then disappear. There is a direct relationship between eye micromotion and the generation of vision, and binocular micromotion is better than monocular micromotion. In recent years, Condem et al. [7] have further revealed that eye micromotion is directly related to image disappearance by measuring the probability, rate and amplitude of micromotion before and after the image disappears or reappears, and that micromotion is causally related to the clarity of the fixation image.

The imaging principle of human eye micro-movement [8,9] is as follows: human eye micro-movement makes the information of interest fall into the fovea more accurately. The more accurately the information falls into the fovea, the sharper the image perceived by the human eye. Human eye micro-movement will enhance the reaction of the near fovea, and they will correct the information of the fovea through the lateral connection of the inhibitory neural network. If the correction is not ideal, human eye micro-movement will make the information return to the fovea more accurately, and adjust the micro-movement amplitude according to the size of the information scale, and repeat this process until the information is accurately identified. The smaller the information scale, the smaller the micro-movement amplitude, and vice versa. At the same time, as the micro-movement rate increases, the sharper the corresponding retinal field of view will be. This paper approximates the micro-movement mechanism of the human eye by translating the image and applies it to the edge extraction of infrared images.

2 Basic Process of Algorithm

2.1 Basic Principles of the Algorithm

An algorithm can be understood as a complete problem-solving procedure consisting of basic operations and a prescribed order of operations. Or it can be seen as a finite and exact sequence of calculations designed according to requirements, and such steps and sequences can solve a class of problems.

The human eye mainly relies on the micro-motion mechanism to distinguish the edge of the image. The smaller the amplitude of the eyeball micro-motion, the finer the edge of the image. As the amplitude increases, the edge lines of the image become thicker, but the ability to highlight large-scale edges is stronger than that of small amplitudes. The human eye micro-motion theory has a strong edge extraction ability. Combined with the sensitivity of retinal ganglion cells to direction, the direction of the human eye micro-motion is first selected, and then the edge image of the micro-motion direction is calculated through the micro-motion image. After that, the edge images of each micro-motion direction enter the competition stage. The result of the competition is the optimal overall grayscale edge image of each micro-motion direction. Finally, the binary processing is performed to generate a binary edge image.

Assume that the original image is f(x, y), then the micro-motion edge image of the image in a certain micro-motion direction is expressed by the following formula:

g(x,y)={f(x,y)-f(x+k1·?stationx,y+k2·?stationy)} (1)

In the formula, ? 停x, ? 停y represent the distance units of the image moving in the x and y directions respectively, k1 and k2 represent the size of the movement, and ? 兹 represents the direction of the movement. The calculation formula is:

?Z = arctan (k2/k1) (2)

Let r(x,y) represent the output of the competitive network, then the competitive grayscale edge image can be expressed as:

r(x,y)=max{gi(x,y)|?匌i} (3)

Set the threshold t to get the binary edge image:

R(x,y)=1 r(x,y)≥t0 r(x,y)

2.2 Calculation steps

(1) Input the original image A and generate the comprehensive micro-motion image F by translating the original image A in the micro-motion direction. F = [Ah, Av, Ad], where h, v, and d represent the horizontal, vertical, and oblique directions, respectively. In this paper, image A is translated in eight directions, with the moving distance being one pixel.

(2) Calculate the edge image H in each micro-motion direction:

Ci=Fi-A, i=h,v,d (5)

(3) Calculate the competitive grayscale edge image H:

H=max(Ci), i=h,v,d (6)

(4) Requantize the competitive grayscale edge image H to [0, 255].

(5) In order to reduce the generation of pseudo edges, the competitive edge image H is processed by mean filtering:

G = mean (H) (7)

(6) For the grayscale edge image after quantization filtering, non-maximum suppression and double threshold detection edge connection processing are used to obtain a binary edge image.

2.3 Non-maximum suppression

Directly binarizing the competitive grayscale edge image after quantization filtering cannot accurately extract the edge of the image. Therefore, it is necessary to perform non-maximum suppression on the amplitude of the competitive grayscale edge image after quantization filtering to further determine the edge point. If the edge intensity G(i,j) of the pixel point (i,j) on the image G(x,y) is less than the edge intensity of the two adjacent pixels along the translation line, the pixel point is considered to be a non-edge point and its grayscale value is set to 0. That is, the point with the largest local amplitude change is retained to refine the ridge band in the amplitude image.

2.4 Dual Threshold Detection and Edge Connection

Since both noise and edges in the image belong to high-frequency parts, a large part of the edge image after non-maximum suppression processing is still pseudo-edge points belonging to noise, so denoising processing is necessary [7]. This paper adopts a high-low dual threshold method to implement this denoising process. Set two high and low thresholds. The edge image after high threshold processing can remove most of the noise and obtain a clear edge with a larger size, but at the same time loses some useful detailed edge information; the image after low threshold denoising retains more information and can retain subtle edges, but produces more pseudo-edges. After double threshold processing, two binary edge images with different features can be obtained. Based on the high threshold edge image and supplemented by the low threshold edge image, edge connection is performed to achieve the final image edge extraction.

3 Experimental Results

The edge extraction effect of the Sobel algorithm is poor, the target edge breakage phenomenon is more serious, and it is more sensitive to noise; although the morphological edge extraction extracts the edge of the target more completely, it produces a large number of pseudo edges; using the algorithm in this paper to extract the edge, the target edge extraction effect is more ideal, it is not sensitive to noise, and fewer pseudo edges are extracted (the pseudo edges around the image are generated during the translation process of the image and do not affect the subsequent processing of the image).

Experimental results show that the edge extraction algorithm based on the human eye micro-motion visual imaging mechanism can quickly and accurately extract the edges of infrared images, and can better suppress the generation of pseudo edges, achieving good edge extraction effects.