An image processing method for enhancing a target object in a non-visual range imaging image and a related device

Abstract

The application discloses an image processing method for enhancing a target object in a non-visual imaging image and a related device. The image processing method for enhancing a target object in a non-visual imaging image comprises the following steps: calculating a primary non-visual imaging image obtained, calculating light intensity of a light intensity maximum point and a light intensity minimum point respectively, and calculating light intensity of feature points on a fuzzy outline and other position feature points. According to distribution characteristics and intervals of the light intensity, an operation method and an iteration number of the picture are determined, a difference between target intensity and background intensity is increased, target highlighting is realized, and outline optimization of the non-visual imaging target image is completed. The related device comprises an image input interface, an image calculation and processing system, and an image storage and output system. The application provides an image processing method and a related device for improving non-visual imaging quality, has a good highlighting effect on a non-visual imaging target, can strengthen contrast between the target and the background, and is easier to identify outline characteristics of the target object.

Description

Technical Field

[0001] This invention belongs to the field of non-view imaging, relates to the field of image processing, and particularly relates to a method and apparatus for image calculation, iteration and computational imaging. Background Technology

[0002] Traditional optical imaging methods cannot detect targets outside the line of sight and cannot image occluded targets. Non-line-of-sight (NLOS) imaging technology breaks through this paradigm, enabling imaging of targets behind obstacles. It captures light carrying target information after reflection from an intermediate surface or after scattering from an obstacle to image targets outside the line of sight. This technology is highly applicable in many special scenarios, such as disaster search and rescue and urban reconnaissance, where complex environments prevent direct target detection. NLOS imaging effectively addresses this problem. However, this technology places high demands on the scene, intermediate surfaces, and equipment during reconstruction, and even minor changes to these factors significantly impact the quality of the reconstructed image. Therefore, based on existing imaging environments and equipment, the quality of NLOS imaging can be improved through post-processing. This paper proposes an image processing method and related apparatus to enhance target objects in NLOS imaging images. Summary of the Invention

[0003] In view of this, the present invention proposes an image processing method and related apparatus for enhancing target objects in non-view imaging images, which can significantly improve the edge sharpness of targets in non-view imaging images.

[0004] To achieve the above objectives, the technical solutions of the embodiments of the present invention are as follows:

[0005] According to one aspect of the present invention, an image processing method is provided. An exemplary embodiment includes calculating the light intensity values ​​of the highest and lowest light intensity points in a target image, selecting feature points on blurred boundaries or other locations, and calculating their light intensity. Based on the distribution range and pattern of the calculated light intensity values ​​at each point, a suitable image iteration method and number of iterations are selected. The smallest unit of the light intensity image is similar to a pixel unit, and processed according to the selected iteration method. A custom smallest unit can also be defined, and the number and size of the smallest units can be reasonably set according to the imaging quality requirements. The calculation method and number of iterations are unlimited. The light intensity of the highest and lowest light intensity points in the obtained secondary imaging image is recalculated. Based on the secondary imaging quality, feature points still existing in the blurred boundaries of the secondary imaging image are selected for light intensity calculation. Based on the distribution range and characteristics of all light intensity values ​​in the secondary imaging image, a suitable iteration method and number of iterations are selected again for processing. The above process is repeated until multiple imaging images achieve good results, resulting in an image with clear target boundaries.

[0006] In some embodiments, there is more than one point of maximum light intensity and one point of minimum light intensity in the image, and the point of maximum light intensity is not necessarily on the target object, nor is the point of minimum light intensity necessarily in the background.

[0007] In some embodiments, the selection of blurred boundary feature points and other location feature points is not limited, and the number of blurred boundary feature points and other location feature points is unlimited, and can be selected according to the imaging effect;

[0008] In some embodiments, the image iteration method can be arbitrarily selected, and multiple iteration methods can be used in a single calculation;

[0009] In some embodiments, the smallest unit of the light intensity image can be arbitrarily set, which can be each pixel or a custom minimum size, and can be reasonably set according to the imaging quality requirements.

[0010] In some embodiments, the number of calculation iterations can be appropriately increased or decreased without limitation, and an appropriate number of calculation iterations can be selected based on the imaging quality;

[0011] In some embodiments, the method and number of calculations and iterations selected for each iteration may be different from or the same as those in the previous iterations.

[0012] According to another aspect of the present invention, an image processing related apparatus is provided, comprising:

[0013] The image input module is used to input a non-view field imaging raw image, wherein the raw image contains the target;

[0014] The image calculation and processing module is used to calculate and iterate on the image to make the boundaries of the imaged target clear;

[0015] The image output module is used to output the processed high-quality imaging images.

[0016] Beneficial effects:

[0017] This invention calculates the light intensity values ​​of the highest and lowest light intensity points, as well as blurred boundary points and feature points in the image. These values ​​serve as the basis for selecting the calculation iteration method, effectively reducing the workload of non-viewpoint imaging image processing, minimizing additional calculations due to trial and error, and eliminating the influence of various factors. Using the previous imaging quality as the basis for subsequent imaging calculations effectively eliminates the possibility of poor results after multiple calculations, thus verifying the possible locations of deviations and providing good directional control over the final imaging effect. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall principle of an embodiment of the present invention;

[0019] Figure 2 This is a schematic flowchart of the image processing method according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the light intensity of the maximum light intensity point, the minimum light intensity point, and the feature point in the image of an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the non-viewpoint imaging image calculation iteration process according to an embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram of the image processing device of the present invention. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] The described embodiments are only some embodiments of the present invention, and not all embodiments of the present invention. The present invention is not limited to the exemplary embodiments.

[0025] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0026] As will be understood by those skilled in the art, in the embodiments of the present invention, "multiple" refers to two or more, and "at least one" refers to one, two or more.

[0027] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0028] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the invention or its application or use in any way.

[0029] The embodiments of this invention can be applied to various terminal devices, computer systems, servers, and other electronic devices, and can operate together with numerous other general-purpose or special-purpose computing system environments or configurations. These include, but are not limited to: personal computer systems, server computer systems, handheld or laptop devices, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the above systems, etc.

[0030] like Figure 1As shown, the present invention provides an image processing method and related apparatus for enhancing target objects in non-view-of-sight imaging images. It is characterized by comprising three parts: an input and calculation system for the non-view-of-sight imaging image to be processed, an image calculation and iteration system, and an output system for ultimately outputting the image. Figure 2 As shown, this embodiment calculates the light intensity values ​​of the highest and lowest light intensity points in the target image. It also calculates the light intensity of feature points or multiple feature points on the blurred boundary. Based on the distribution range and characteristics of the calculated light intensity values ​​at each point, a suitable image processing iteration method and number of iterations are selected. The smallest unit of the light intensity image is processed according to the selected iteration method to obtain a secondary imaging image. The quality of the secondary imaging image is judged. If the effect is unsatisfactory, the light intensity of the highest and lowest light intensity points in the obtained secondary imaging image is calculated again. For feature points still existing on the blurred boundary in the secondary imaging image, their light intensity is calculated, and based on the distribution range and characteristics of the light intensity values ​​of the points in the secondary imaging image, a suitable iteration method and number of iterations are selected again for processing. This process is repeated until an imaging image with a clear target boundary is obtained.

[0031] The image processing method and related devices for non-viewpoint imaging according to the present invention are as follows:

[0032] Firstly, as Figure 3 As shown, select the maximum light intensity point 8, the minimum light intensity points 1 and 2, and the blurred edge feature points 3, 4, 5, 6, and 7 in the input non-view field initial imaging image. Calculate the light intensity values ​​of each point to obtain the following values: Point 1 and Point 2 light intensity value 11, Point 3 light intensity value 46, Point 4 light intensity value 33, Point 5 light intensity value 39, Point 6 light intensity value 42, Point 7 light intensity value 40, and Point 8 light intensity value 58.

[0033] Based on the obtained light intensity values, the maximum light intensity is 58, the minimum light intensity is 11, and the distribution range of light intensity values ​​at the edge points is [33, 46]. Since the light intensity range at the edge points is close to the maximum light intensity, and its minimum value is 3 times the minimum light intensity, the light intensity superposition operation is preferentially performed on the smallest unit in the image.

[0034] After one calculation, the light intensity of the maximum point in the image is 116, and the minimum is 22. The light intensity range of the selected edge feature points becomes [66, 92]. It can be seen that the contrast between the blurred boundary and the background changes from 22 to 44. That is, after one calculation, the distinguishability of the blurred boundary is increased.

[0035] If the effect is still unsatisfactory, the image can be overlaid again, or the feature points can be re-selected for light intensity calculation. Then, a reasonable calculation rule can be chosen based on the new light intensity value distribution range and characteristics. There are no restrictions on the location or number of feature points, nor on the calculation method or number of iterations. Multiple calculation methods can also be used cumulatively in a single calculation, such as first overlaying the image and then dividing it by half the original image value, etc., combined appropriately according to different light intensity distributions. The final output image will have more prominent boundary information of the target object.

[0036] This invention provides an image processing method and related apparatus for enhancing target objects in non-view imaging images. The apparatus and equipment realize image input, computational iteration, and output. When performing computational iteration on the image, the image processing method of this invention is used to optimize the blurred edges of non-view imaging images, effectively improving the problem of blurred boundaries in non-view imaging target images and providing a foundation for subsequent restoration of non-view target details.

[0037] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An image processing method for enhancing a target object in a non-visual range imaging image, characterized by, Includes the following steps: Calculate the light intensity values ​​at the points of highest and lowest light intensity in the target image; Select fuzzy boundary feature points or other location feature points and calculate the light intensity; Choose an appropriate image iteration method and number of iterations based on the calculated light intensity values ​​and their distribution patterns at each point. The smallest unit of the initial image is processed according to the selected iterative method to obtain the secondary image; The light intensity of the points with the maximum and minimum light intensity values ​​in the obtained secondary imaging image is calculated again. Feature points are selected and calculated for the blurred boundaries that still exist in the secondary imaging image; Based on the light intensity values ​​and distribution patterns of the secondary imaging images, a suitable iteration method and number of iterations are selected for further processing. After multiple calculations and processing, an image with clear target boundaries is obtained.

2. The image processing method of claim 1, wherein The highest point of light intensity may not be on the target object in the image, but may be in the background environment.

3. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The lowest point of light intensity is not necessarily in the image background environment, but may be on the target.

4. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The point with the lowest light intensity is the point with the smallest light intensity value among all points with light intensity values, and its value is 0.

5. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, There may be more than one point of maximum light intensity and one point of minimum light intensity.

6. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The calculated light intensity values ​​at the highest and lowest light intensity points provide a reference range for separating the target from the background.

7. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The selection of points on the blurred boundary in the image is determined according to the imaging purpose and quality requirements.

8. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The number of points on the blurred boundary in the image is determined based on the degree of blurring, the length and shape of the boundary, and the light intensity is calculated. The obtained light intensity value is used to make fine divisions of the boundary.

9. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The image iteration method is selected from any one or more of the following: pixel overlay, pixel weighted average, pixel difference operation, and pixel composite operation.

10. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The number of image iterations is determined based on the imaging quality requirements.

11. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The positions of the maximum and minimum light intensity points in the secondary imaging image may differ from those in the corresponding points in the primary imaging image due to the selection of different image iteration methods.

12. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 1, characterized in that, The iteration method and number of iterations selected for the recalculation and subsequent calculations are different from the iteration method and / or number of iterations selected in the previous iterations.

13. The image processing method for enhancing target objects in non-viewpoint imaging images as described in claim 12, characterized in that, The number of iterations selected for recalculation and subsequent calculations is different from the number of iterations in the previous iterations.

14. An image processing related apparatus, characterized in that, include: The image input module is used to input a non-view field imaging raw image, wherein the raw image contains the target; The image calculation and processing module is used to execute the image processing method for enhancing the target object in the non-view imaging image as described in claim 1, and to perform calculation and iterative processing on the image already formed. The calculation and iterative processing includes: calculating the light intensity values ​​of the highest light intensity point, the lowest light intensity point and the blurred boundary feature points in the image, selecting the iteration method and number of iterations according to the light intensity values ​​and their distribution rules, and repeating the above calculation and iterative process until the boundary of the imaging target is clear. The image output module is used to output the processed high-quality imaging images.

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