Infrared image enhancement method based on polarization information

Abstract

The invention relates to an image enhancement method, in particular to an infrared image enhancement method based on polarization information. The method comprises the following steps: 1, obtaining polarization images of a target scene at four different angles of an infrared band; 2, acquiring Stokes vector images S0, S1 and S2, a polarization degree image and a polarization angle image; 3, acquiring a temperature image T; 4, acquiring a material image M; 5, obtaining a texture image C; and step 6, combining the material image M, the temperature image T and the texture image C to generate an HSV enhanced image of the target scene so as to realize infrared image enhancement. According to the method, four-angle polarization images of a target scene in an infrared band are obtained through an infrared polarization camera, detection perception of environment target temperature, materials and textures is achieved through polarization information, and then an HSV enhanced image rich in contrast ratio and texture information is obtained through an HSV color space fusion method according to the three-dimensional physical information.

Description

Technical Field

[0001] The invention relates to an image enhancement method, in particular to an infrared image enhancement method based on polarization information. Background Art

[0002] In order to meet the needs of night vision, infrared imaging is currently used. Infrared thermal radiation is an ideal signal source for achieving night vision needs. According to Planck's law, all objects with temperature are radiating heat signals. Thermal radiation is scattered and propagated, and diffuses to every corner of the world. Therefore, thermal radiation not only has the advantages of "natural harmlessness", "passive", "hidden", and "night vision", but also carries important physical information such as object temperature, material, and surface texture. However, traditional infrared imaging has a typical "ghost" effect. Infrared images have low contrast and lack texture details. These defects are the main obstacles to using infrared imaging for night information extraction, and are also the main bottlenecks for its widespread application in visual navigation, fine target detection, etc.

[0003] At present, there are two main types of infrared image enhancement methods: one is to enhance the signal-to-noise ratio in the spatial domain or frequency domain based on the infrared image itself to complete the image enhancement; the other is to combine multi-dimensional information (such as spectral dimension) for intensity fusion enhancement. The common point of the two types is that whether it is based on the infrared image itself or the fusion of multi-dimensional information, it is reflected in the enhancement of image intensity information. The lack of application of multiple physical information in the infrared band leads to the fact that the acquired infrared images still have problems such as low contrast and lack of texture information. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problems that infrared images acquired by existing infrared image enhancement methods have low contrast and lack of texture information, and to provide an infrared image enhancement method based on polarization information.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for enhancing an infrared image based on polarization information is characterized in that it comprises the following steps:

[0007] Step 1: Obtain polarization images of the target scene at four different angles in the infrared band;

[0008] Step 2: Based on the polarization images at four different angles, obtain Stokes vector images S0, S1 and S2, and then obtain polarization degree images and polarization angle images based on the Stokes vector images S0, S1 and S2;

[0009] Step 3, converting the acquired Stokes vector image S0 into the temperature information of each pixel, and then normalizing the temperature information of each pixel to convert it into a temperature image T with a gray value of 0 to 1;

[0010] Step 4: cluster analysis is performed on the polarization degree image and the polarization angle image, and the intersection of the two image categories is taken to determine the number of material types in the polarization degree image and the polarization angle image, and based on the preset corresponding relationship between the number of material types and the grayscale value, a material image M with a grayscale value of 0 to 1 is generated;

[0011] Step 5: normalize the polarization images at four different angles to obtain a normalized image set, and then convert the normalized image set into grayscale images C1, C2, C3 and C4 with grayscale values ​​of 0 to 1, and then average the grayscale images C1, C2, C3 and C4 to obtain a texture image C;

[0012] Step 6: Combine the material image M, the temperature image T and the texture image C to generate an HSV enhanced image of the target scene to achieve infrared image enhancement; wherein the material information in the material image M is used as the hue H in the HSV enhanced image, the temperature information in the temperature image T is used as the saturation S in the HSV enhanced image, and the texture information in the texture image C is used as the brightness V in the HSV enhanced image.

[0013] Furthermore, step 1 is specifically as follows:

[0014] An infrared polarization camera is used to obtain polarization images of the target scene at four different angles of 0°, 45°, 90° and 135° in the infrared band, which are recorded as I(0°), I(90°), I(45°) and I(135°) respectively;

[0015] Step 2 is as follows:

[0016] Based on the polarization images I(0°), I(90°), I(45°) and I(135°), the Stokes vector images S0, S1 and S2 are calculated using the following formula:

[0017]

[0018] Based on the Stokes vector images S0, S1 and S2, the polarization degree image and polarization angle image are calculated using the following formula:

[0019]

[0020] Where: DoLP is the degree of polarization image, AoLP is the angle of polarization image;

[0021] Step 5 is as follows:

[0022] The polarization images I(0°), I(90°), I(45°) and I(135°) are normalized to obtain a normalized image set, and then the normalized image set is converted into grayscale images C1, C2, C3 and C4 with grayscale values ​​of 0 to 1, and then the grayscale images C1, C2, C3 and C4 are averaged to obtain the texture image C.

[0023] Furthermore, in step 4, the corresponding relationship between the preset number of material types and grayscale values ​​is specifically as follows:

[0024] When the number of material types in the polarization image is 1, the grayscale values ​​of the material image M are all 1;

[0025] When the number of material types in the polarization degree image is 2, the grayscale values ​​of the material image M are set to 0 and 1 respectively according to the material temperature from low to high;

[0026] When the number of material types in the polarization image is N, N ≥ 3, the grayscale values ​​of the material image M are set to 0, 1.

[0027] Furthermore, in step 4, cluster analysis is performed on the polarization degree image and the polarization angle image by using a K-means clustering algorithm, and an initial K value of the K-means clustering algorithm is estimated by an elbow method.

[0028] Furthermore, in step 6, the material image M, the temperature image T and the texture image C are combined by the HSV color space fusion method to generate an HSV enhanced image of the target scene.

[0029] Furthermore, the acquired Stokes vector image S0 is converted into temperature information of each pixel according to the calibration information of the infrared polarization camera.

[0030] The beneficial effects of the present invention are:

[0031] 1. The present invention provides an infrared image enhancement method based on polarization information. The infrared polarization camera is used to obtain polarization images of the target scene at four angles in the infrared band. The polarization information is used to detect and perceive the temperature, material, and texture of the environmental target. The above three-dimensional physical information is then fused through the HSV color space method to obtain an HSV enhanced image with rich contrast and texture information.

[0032] 2. The present invention can greatly improve the visual detection accuracy in low-light environments such as at night, and has great application value in the fields of unmanned driving, target detection, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a flowchart of an infrared imaging method embodiment based on polarization information. DETAILED DESCRIPTION

[0034] In order to make the purpose, advantages and features of the present invention more clear, the infrared image enhancement method based on polarization information proposed by the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. According to the following specific implementation methods, the advantages and features of the present invention will be more clear.

[0035] like Figure 1 As shown, the infrared image enhancement method based on polarization information in this embodiment specifically includes the following steps:

[0036] Step 1: Use an infrared polarization camera to obtain polarization images I(0°), I(90°), I(45°), and I(135°) at four different angles (0°, 45°, 90°, and 135°) in the infrared band.

[0037] This embodiment uses an infrared polarization camera as a data acquisition device. The size of the device is 320×240, the number of bits is 16, and the temperature range that can be measured is -20°C to +120°C.

[0038] Step 2: Based on the polarization images at four angles, I(0°), I(90°), I(45°), and I(135°), the Stokes vector image (S0, S1, S2), the degree of polarization image DoLP, and the angle of polarization image AoLP are calculated using the following formula:

[0039]

[0040] Step 3: convert the Stokes vector image S0 obtained by the infrared polarization camera in step 2 into the temperature information of each pixel according to the calibration information of the infrared polarization camera, and then normalize the temperature information of each pixel to convert it into a temperature image T with a gray value of 0 to 1.

[0041] Step 4: Use the K-means clustering algorithm to perform cluster analysis on the degree of polarization image DoLP and the angle of polarization image AoLP. The two image categories are intersected, and the initial K value of the K-means clustering algorithm is estimated by the elbow method (also known as the elbow method). Determine the number of material types in the degree of polarization image and the angle of polarization image, and generate a material image M based on the preset correspondence between the number of material types and the grayscale value. The grayscale value range of the material image M is 0 to 1.

[0042] Specifically, the corresponding relationship between the number of material types and grayscale values ​​is as follows:

[0043] Ⅰ. When the number of material types in the degree of polarization image DoLP is 1, the grayscale values ​​of the material image M are all 1;

[0044] II. When the number of material types in the degree of polarization image DoLP is 2, the grayscale values ​​of the material image M are set to 0 and 1 respectively according to the material temperature from low to high;

[0045] III. When the number of material types in the degree of polarization image DoLP is N, N ≥ 3, the grayscale values ​​of the material image M are set to 0, 1.

[0046] In this embodiment, an example is given in which the number of material types in the degree of polarization image DoLP is four, and the grayscale values ​​of the material image M are respectively set to 0, 0.34, 0.67 and 1 according to the material temperature from low to high.

[0047] Step 5: Normalize the polarization images I(0°), I(90°), I(45°), and I(135°) to obtain a normalized image set, and then convert the normalized image set into grayscale images C1, C2, C3, and C4 with grayscale values ​​of 0 to 1. Then, average the grayscale images C1, C2, C3, and C4 to obtain a texture image C, which is expressed as follows:

[0048]

[0049] Step 6: Combine the material image M, the temperature image T and the texture image C to generate the HSV enhanced image Z to achieve infrared image enhancement. The material information in the material image M is used as the hue H in the HSV enhanced image Z, the temperature information in the temperature image T is used as the saturation S in the HSV enhanced image Z, and the texture information in the texture image C is used as the brightness V in the HSV enhanced image Z. That is, the material image M is used as the first dimension, the temperature image T is used as the second dimension, and the texture image C is used as the third dimension. The expression is: Z = [M, T, C].

Claims

1. An infrared image enhancement method based on polarization information, characterized in that: The following steps are involved: Step 1: Obtain polarization images of the target scene at four different angles in the infrared band; Step 2: Based on the polarization images at four different angles, obtain Stokes vector images S0, S1 and S2, and then obtain polarization degree images and polarization angle images based on the Stokes vector images S0, S1 and S2; Step 3, converting the acquired Stokes vector image S0 into the temperature information of each pixel, and then normalizing the temperature information of each pixel to convert it into a temperature image T with a gray value of 0 to 1; Step 4: cluster analysis is performed on the polarization degree image and the polarization angle image, and the intersection of the two image categories is taken to determine the number of material types in the polarization degree image and the polarization angle image, and based on the preset corresponding relationship between the number of material types and the grayscale value, a material image M with a grayscale value of 0 to 1 is generated; Step 5: Normalize the polarization images at four different angles to obtain a normalized image set, then convert the normalized image set into grayscale images C1, C2, C3 and C4 with grayscale values ​​of 0 to 1, and then average the grayscale images C1, C2, C3 and C4 to obtain a texture image C; Step 6: Combine the material image M, the temperature image T and the texture image C to generate an HSV enhanced image of the target scene to achieve infrared image enhancement; wherein the material information in the material image M is used as the hue H in the HSV enhanced image, the temperature information in the temperature image T is used as the saturation S in the HSV enhanced image, and the texture information in the texture image C is used as the brightness V in the HSV enhanced image.

2. The infrared image enhancement method based on polarization information according to claim 1, characterized in that: Step 1 is as follows: An infrared polarization camera is used to obtain polarization images of the target scene at four different angles of 0°, 45°, 90° and 135° in the infrared band, which are recorded as I(0°), I(90°), I(45°) and I(135°) respectively; Step 2 is as follows: Based on the polarization images I(0°), I(90°), I(45°) and I(135°), the Stokes vector images S0, S1 and S2 are calculated using the following formula: Based on the Stokes vector images S0, S1 and S2, the polarization degree image and polarization angle image are calculated using the following formula: Where: DoLP is the degree of polarization image, AoLP is the angle of polarization image; Step 5 is as follows: The polarization images I(0°), I(90°), I(45°) and I(135°) are normalized to obtain a normalized image set, and then the normalized image set is converted into grayscale images C1, C2, C3 and C4 with grayscale values ​​of 0 to 1, and then the grayscale images C1, C2, C3 and C4 are averaged to obtain the texture image C.

3. The infrared image enhancement method based on polarization information according to claim 1 or 2, characterized in that: In step 4, the corresponding relationship between the preset material type quantity and gray value is specifically: When the number of material types in the polarization image is 1, the grayscale values ​​of the material image M are all 1; When the number of material types in the polarization degree image is 2, the grayscale values ​​of the material image M are set to 0 and 1 respectively according to the material temperature from low to high; When the number of material types in the polarization image is N, N ≥ 3, the grayscale values ​​of the material image M are set to 0, 1.

4. The infrared image enhancement method based on polarization information according to claim 3, characterized in that: In step 4, the polarization degree image and the polarization angle image are clustered and analyzed by using the K-means clustering algorithm, and the initial K value of the K-means clustering algorithm is estimated by the elbow method.

5. The infrared image enhancement method based on polarization information according to claim 4, characterized in that: In step 6, the material image M, the temperature image T and the texture image C are combined by the HSV color space fusion method to generate an HSV enhanced image of the target scene.

6. The infrared image enhancement method based on polarization information according to claim 2, characterized in that: The acquired Stokes vector image S0 is converted into the temperature information of each pixel according to the calibration information of the infrared polarization camera.

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