Thermal Camera and Method for Processing Thermal image thereof

Abstract

An embodiment of the present invention discloses a thermal image processing method comprising: a step of determining a blending ratio of different first smoothing algorithms and second smoothing algorithms based on the analysis result of a original histogram for an original thermal image; a step of generating a first corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the blending ratio; a step of adjusting the blending ratio by comparing the corrected histogram for the first corrected thermal image with the original histogram; and a step of generating a second corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the adjusted blending ratio.

Description

Technology Field

[0001] An embodiment of the present invention relates to a thermal imaging camera and a thermal image processing method thereof. Background Technology

[0002] Recently, in surveillance systems, it is becoming increasingly common to perform surveillance using thermal imaging cameras or to use both standard cameras and thermal imaging cameras together.

[0003] When using thermal imaging cameras depending on the surveillance environment or purpose, it is necessary to process the thermal image data into a format that users can utilize. The problem to be solved

[0004] Embodiments of the present invention aim to provide a thermal image with optimally improved contrast while minimizing the loss of detail elements by adjusting the histogram equalization method and ratio for processing thermal image data according to the installation environment of the thermal imaging camera. means of solving the problem

[0005] A thermal image processing method according to an embodiment of the present invention comprises: a step of determining a blending ratio of different first smoothing algorithms and second smoothing algorithms based on the analysis result of a original histogram for an original thermal image; a step of generating a first corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the blending ratio; a step of adjusting the blending ratio by comparing the corrected histogram for the first corrected thermal image with the original histogram; and a step of generating a second corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the adjusted blending ratio.

[0006] The blending ratio determination step may include: a step of dividing the original histogram into a second subhistogram corresponding to a first region with many detail elements in the original thermal image and a first subhistogram corresponding to a second region other than the first region; and a step of determining the ratio of the first subhistogram and the second subhistogram as the blending ratio of the first smoothing algorithm and the second smoothing algorithm.

[0007] The above histogram classification step may include the step of classifying into the first subhistogram if the distribution gap, which is the difference between the minimum index and the maximum index on the horizontal axis of the subhistogram, is smaller than the threshold value, and classifying into the second subhistogram if the distribution gap is greater than or equal to the threshold value.

[0008] The ratio of the first subhistogram and the second subhistogram may be the ratio of the sum of the occurrence frequencies of the first subhistogram to the sum of the occurrence frequencies of the second subhistogram.

[0009] The blending ratio adjustment step may include: a step of detecting second characteristic data of a corresponding first subhistogram corresponding to the first subhistogram and a corresponding second subhistogram corresponding to the second subhistogram in the correction histogram; and a step of adjusting the blending ratio by comparing the first characteristic data of the first subhistogram and the second subhistogram with the second characteristic data.

[0010] The comparison between the first characteristic data and the second characteristic data may include the comparison of the distribution gap of the first subhistogram and the distribution gap of the corresponding first subhistogram, and the comparison of the distribution gap of the second subhistogram and the distribution gap of the corresponding second subhistogram.

[0011] The above method may further include the step of detecting the degree of skewness and clipping of the corresponding first subhistogram and the corresponding second subhistogram in the correction histogram; and the step of adjusting the gain of the corresponding first subhistogram and the corresponding second subhistogram according to the degree of skewness or clipping.

[0012] The first smoothing algorithm above is a method of smoothing a histogram based on an area with a high frequency of occurrence in the histogram, and the second smoothing algorithm above may be a method of smoothing a histogram based on an area with many detail elements.

[0013] A thermal imaging camera according to one embodiment of the present invention comprises: a detector; and a processor that generates a thermal image using a pixel brightness value input from the detector; wherein the processor determines a blending ratio of different first and second smoothing algorithms based on the analysis result of a original histogram for an original thermal image, generates a first corrected thermal image in which the first and second smoothing algorithms are applied to the original thermal image according to the blending ratio, adjusts the blending ratio by comparing the corrected histogram for the first corrected thermal image with the original histogram, and generates a second corrected thermal image in which the first and second smoothing algorithms are applied to the original thermal image according to the adjusted blending ratio.

[0014] The processor can divide the original histogram into a second sub-histogram corresponding to a first region with many detail elements and a first sub-histogram corresponding to a second region other than the first region, and determine the ratio of the first sub-histogram and the second sub-histogram as the blending ratio of the first smoothing algorithm and the second smoothing algorithm.

[0015] The processor can classify the first subhistogram if the distribution gap, which is the difference between the minimum index and the maximum index on the horizontal axis of the subhistogram, is smaller than the threshold value, and classify the second subhistogram if the distribution gap is greater than or equal to the threshold value.

[0016] The ratio of the first subhistogram and the second subhistogram may be the ratio of the sum of the occurrence frequencies of the first subhistogram to the sum of the occurrence frequencies of the second subhistogram.

[0017] The processor can detect second characteristic data of a corresponding first subhistogram corresponding to the first subhistogram and a corresponding second subhistogram corresponding to the second subhistogram in the correction histogram, and adjust the blending ratio by comparing the first characteristic data of the first subhistogram and the second subhistogram with the second characteristic data.

[0018] The comparison between the first characteristic data and the second characteristic data may include the comparison of the distribution gap of the first subhistogram and the distribution gap of the corresponding first subhistogram, and the comparison of the distribution gap of the second subhistogram and the distribution gap of the corresponding second subhistogram.

[0019] The processor can detect the degree of skewness and clipping of the corresponding first sub-histogram and the corresponding second sub-histogram in the correction histogram, and adjust the gain of the corresponding first sub-histogram and the corresponding second sub-histogram according to the degree of skewness or clipping.

[0020] The first smoothing algorithm above is a method of smoothing a histogram based on an area with a high frequency of occurrence in the histogram, and the second smoothing algorithm above may be a method of smoothing a histogram based on an area with many detail elements. Effects of the invention

[0021] Embodiments of the present invention can provide a thermal image with optimally improved contrast while minimizing the loss of detail elements by adjusting the histogram equalization method for processing thermal image data and the application ratio of the histogram equalization method according to the installation environment of the thermal image camera. Brief explanation of the drawing

[0022] FIG. 1 is a schematic diagram showing a thermal imaging camera according to one embodiment. FIG. 2 is a schematic diagram showing the configuration of a processor according to one embodiment. Figure 3a is an example diagram showing a circular thermal image and a circular histogram of the circular thermal image. FIG. 3b is a diagram showing a corrected thermal image and a histogram thereof, to which a first smoothing algorithm is applied to the original thermal image according to one embodiment. FIG. 3c is a diagram showing a corrected thermal image and a histogram thereof, to which a second smoothing algorithm is applied to the original thermal image according to one embodiment. FIG. 4 is a diagram showing an example of distinguishing sub-histograms from a original histogram according to one embodiment. FIG. 5 is a diagram showing a first corrected thermal image and a corrected histogram for the first corrected thermal image according to one embodiment. FIG. 6 is a diagram illustrating a comparison of characteristic data of a histogram for controlling the blending ratio according to one embodiment. FIG. 7 is a diagram schematically illustrating a thermal image processing method according to one embodiment. FIG. 8 is a schematic diagram showing a thermal imaging camera according to one embodiment. Specific details for implementing the invention

[0023] The following description merely illustrates the principles of the present invention. Therefore, those skilled in the art may invent various devices that embody the principles of the present invention and are included within the concept and scope of the present invention, even though they are not explicitly described or illustrated in this specification. Furthermore, all conditional terms and embodiments listed in this specification should be understood as being explicitly intended, in principle, only for the purpose of understanding the concept of the present invention and not as being limited to the embodiments and conditions specifically listed as such. Additionally, all detailed descriptions enumerating specific embodiments, as well as the principles, aspects, and embodiments of the present invention, should be understood as being intended to include structural and functional equivalents thereof. Furthermore, such equivalents should be understood to include not only currently known equivalents but also equivalents to be developed in the future, that is, all elements invented to perform the same function regardless of structure.

[0024] Accordingly, the functions of the various elements depicted in the drawings, including functional blocks indicated as processors or similar concepts, may be provided by dedicated hardware as well as by the use of hardware capable of executing software in conjunction with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, a single shared processor, or multiple individual processors, some of which may be shared. Furthermore, the use of terms presented as processors, controls, or similar concepts should not be interpreted as exclusively referring to hardware capable of executing software, but should be understood to implicitly include, without limitation, digital signal processor (DSP) hardware, ROM, RAM, and non-volatile memory for storing software. Other hardware of common knowledge may also be included.

[0025] The aforementioned objectives, features, and advantages will become more apparent through the following detailed description in conjunction with the attached drawings. In describing the present invention, if it is determined that a detailed description of related prior art may unnecessarily obscure the essence of the invention, such detailed description shall be omitted or briefly explained.

[0026] Meanwhile, when it is said that a part "includes" a certain component, unless specifically stated otherwise, this means that it does not exclude other components but may include additional components.

[0027] Hereinafter, the present invention according to a preferred embodiment will be described in detail with reference to the attached drawings.

[0028] Hereinafter, the present invention according to a preferred embodiment will be described in detail with reference to the attached drawings. In addition, in this specification and drawings, components having substantially the same configuration are denoted by the same reference numerals to avoid redundant descriptions.

[0029] FIG. 1 is a schematic diagram showing a thermal imaging camera according to one embodiment.

[0030] A thermal imaging camera (1) can detect radiant energy (thermal energy) emitted by an object and measure the intensity of the thermal energy to generate thermal images of different colors according to the intensity. The thermal image can be represented by mapping the surface of the object into different colors according to the temperature level. The thermal imaging camera (1) is placed at a fixed position in a specific location and can have pan / tilt / zoom functions. The thermal imaging camera (1) can acquire a thermal image by photographing an assigned surveillance area.

[0031] Referring to FIG. 1, the thermal imaging camera (1) may include an input unit (10), a processor (30), an output unit (50), and a storage unit (70).

[0032] The input unit (10) may include a lens that transmits / concentrates energy in an effective wavelength band (e.g., an infrared wavelength band), a thermal imaging sensor (detector) that detects energy in the effective wavelength band, and an Analogue-Digital Converter (ADC) that digitizes an analog signal output from the thermal imaging sensor. The input unit (10) may further include a predetermined filter, etc. The thermal imaging sensor may be a configuration corresponding to an image sensor of a color imaging camera, for example, a charge-coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS). The thermal imaging sensor may be a focal plane array (FPA) composed of pixels of micrometer size made of a material capable of detecting infrared wavelengths. Hereinafter, a value representing the magnitude of infrared energy output by each pixel is referred to as the 'pixel brightness value'. The ADC can convert the pixel brightness value, which is an analog signal output by the thermal imaging sensor, into a digital signal.

[0033] The processor (30) can generate a thermal image using pixel brightness values ​​output from the input unit (10). The processor (30) can perform various data processing to improve the image quality of the thermal image data. The processor (30) can improve the image quality of the thermal image by performing histogram equalization and / or histogram stretching on the thermal image data.

[0034] The processor (30) may be implemented with various number of hardware or / and software configurations that execute specific functions. For example, the processor (30) may refer to a data processing device embedded in hardware having a physically structured circuit to perform a function expressed by code or instructions included in a program. Examples of such data processing devices embedded in hardware may include a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), an image signal processor (ISP), etc., but the scope of the invention is not limited thereto.

[0035] The output unit (50) may include a display that displays the results of data processing by the processor (30). The display may be composed of a liquid crystal display panel (LCD), an organic light-emitting diode display panel (OLED), an electrophoretic display panel (EPD), etc.

[0036] The storage unit (70) may store a program for processing and controlling the processor (30), and may also store input / output data of the processor (30) (e.g., thermal images). The storage unit (70) may include memory composed of RAM (random access memory), ROM (read-only component), and a combination thereof. The storage unit (70) may be a large-capacity storage device including a hard disk drive, an optical disk drive, an SSD (solid-state memory device), etc. The storage unit (70) may be built into or mounted in the camera (1).

[0037] FIG. 2 is a schematic diagram showing the configuration of a processor according to one embodiment.

[0038] Referring to FIG. 2, the processor (30) may include a preprocessing unit (302) and a ratio adjustment unit (306).

[0039] The preprocessing unit (302) can improve contrast by performing histogram equalization and / or histogram stretching on the original thermal image data to adjust the distribution of pixel brightness values ​​of the original thermal image. The histogram can be defined as a graph representing the distribution of pixel brightness values ​​of the thermal image, with pixel brightness values ​​as the horizontal axis and the frequency of occurrence (or number of pixels) of each pixel brightness value as the vertical axis.

[0040] The preprocessing unit (302) can generate a corrected thermal image with improved contrast by preprocessing the original thermal image using a plurality of histogram equalization algorithms. The preprocessing unit (302) can generate a corrected thermal image by applying different plurality of histogram equalization algorithms to the original thermal image according to a blending ratio control signal received from the ratio control unit (306).

[0041] The preprocessing unit (302) can generate a corrected thermal image by adjusting the application ratio (degree) of each of the different first smoothing algorithm and second smoothing algorithm according to the blending ratio.

[0042] The first equalization algorithm may be a method for equalizing a histogram based on regions with high occurrence frequencies in the histogram. The first equalization algorithm may equalize the histogram using a cumulative distribution function. Additionally, the first equalization algorithm may equalize the histogram by setting an upper limit on the occurrence frequency globally or locally. In one embodiment, the first equalization algorithm may be a Contrast Limited Histogram Equalization algorithm. Contrast Limited Histogram Equalization equalizes the histogram by limiting the size of the vertical axis of the histogram to a specific value and redistributing pixel brightness values ​​above that specific value. In an embodiment of the present invention, the first equalization algorithm may utilize known Contrast Limited Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, etc.

[0043] The second equalization algorithm may be a method for equalizing histograms by considering detail elements. The second equalization algorithm may be a histogram equalization method that minimizes the loss of detail elements of an image. The second equalization algorithm may use edge data of an image to detect regions of interest, motion regions, high-frequency regions, etc., as detail regions that have many detail elements, i.e., require detailed information, and perform histogram equalization based on the histogram of the detail region.

[0044] In embodiments of the present invention, the smoothing algorithm based on image detail elements is not particularly limited, and a known histogram equalization algorithm that has less loss of image detail elements compared to a first smoothing algorithm may be used. For example, among a plurality of known different histogram equalization algorithms, a histogram equalization that does not consider detail elements and has a relatively simple computational method may be used as the first smoothing algorithm, and a histogram equalization that has relatively less loss of detail elements compared to the first smoothing algorithm may be used as the second smoothing algorithm.

[0045] The ratio adjustment unit (306) can analyze the original histogram calculated from the original thermal image to determine the application ratio of the first smoothing algorithm and the second smoothing algorithm, i.e., the blending ratio.

[0046] The ratio adjustment unit (306) can classify the original histogram into a sub-histogram (second sub-histogram) corresponding to a detail area (first area) and a sub-histogram (first sub-histogram) corresponding to an area other than the detail area (second area).

[0047] The ratio control unit (306) can distinguish sub-histograms based on the index of the horizontal axis that has no or minimum occurrence frequency in the original histogram. Alternatively, the ratio control unit (306) can distinguish sub-histograms based on the index of the horizontal axis that has a high occurrence frequency. The ratio control unit (306) can detect characteristic data from each sub-histogram. The characteristic data may include the minimum index and the maximum index on the horizontal axis of the sub-histogram, and the difference between the minimum index and the maximum index (hereinafter referred to as the 'distribution gap').

[0048] The ratio control unit (306) can classify the subhistogram as a first subhistogram if the distribution gap is smaller than the threshold value, and classify it as a second subhistogram if the distribution gap is greater than the threshold value. If the distribution gap is smaller than the threshold value, it can be determined as an area with few detail elements and a narrow temperature distribution, such as the sky, inside a tunnel, or a wall. If the distribution gap is greater than the threshold value, it can be determined as an area with many detail elements and a wide temperature distribution, such as a building or a road.

[0049] The ratio control unit (306) can determine the ratio of the first subhistogram and the second subhistogram as the first blending ratio of the first equalization algorithm and the second equalization algorithm. The ratio of the first subhistogram and the second subhistogram can be determined based on the amount of the first subhistogram and the amount of the second subhistogram. The ratio control unit (306) can calculate the amount of the first subhistogram by the sum of the occurrence frequencies of the first subhistograms, that is, the sum of the number of pixels having pixel brightness values ​​of the first subhistograms (first pixel count). The ratio control unit (306) can calculate the amount of the second subhistogram by the sum of the occurrence frequencies of the second subhistograms, that is, the sum of the number of pixels having pixel brightness values ​​of the second subhistograms (second pixel count).

[0050] The ratio control unit (306) can determine the ratio of the first number of pixels and the second number of pixels as the first blending ratio of the first smoothing algorithm and the second smoothing algorithm. For example, if the ratio of the first number of pixels and the second number of pixels is m:n, the first blending ratio of the first smoothing algorithm and the second smoothing algorithm can be determined as m:n.

[0051] The ratio control unit (306) can output a control signal including a first blending ratio to the preprocessing unit (302).

[0052] The preprocessing unit (302) can generate a first corrected thermal image by applying a first smoothing algorithm and a second smoothing algorithm according to a first blending ratio to smooth the original histogram. In one embodiment, the preprocessing unit (302) can generate a first corrected thermal image by weighting the corrected thermal image to which the first smoothing algorithm is applied and the corrected thermal image to which the second smoothing algorithm is applied, using the blending ratio as a weight.

[0053] The ratio adjustment unit (306) can adjust the blending ratio by comparing the corrected histogram and the original histogram for the first corrected thermal image. Since the first blending ratio is determined by distinguishing based on the distribution gap of the sub-histogram, the sub-histogram of the first region with a high frequency of occurrence may be misclassified as the first sub-histogram, and if blending is performed by a simple ratio based on the amount of the histogram, the degree of image quality improvement may not be clear. An embodiment of the present invention can generate a corrected thermal image with improved image quality by adjusting the first blending ratio through the analysis of the corrected histogram for the first corrected thermal image generated according to the first blending ratio.

[0054] The ratio control unit (306) can detect characteristic data from the corresponding subhistogram corresponding to each subhistogram of the original histogram in the corrected histogram. The ratio control unit (306) can detect a change between the characteristic data of the original histogram and the characteristic data of the corrected histogram. The ratio control unit (306) can detect a change between the characteristic data of the subhistogram of the original histogram and the characteristic data of the corresponding subhistogram.

[0055] The ratio adjustment unit (306) can detect changes in characteristic data due to histogram equalization by comparing the distribution gap of the first sub-histogram in the original histogram with the distribution gap of the corresponding first sub-histogram, and by comparing the distribution gap of the second sub-histogram with the distribution gap of the corresponding second sub-histogram. The ratio adjustment unit (305) can detect changes in characteristic data based on the result of comparing the distribution gaps between the original histogram and the corresponding sub-histograms of the corrected histogram, and based on the increase or decrease in the ratio of the distribution gap of the corresponding first sub-histogram and the increase or decrease in the ratio of the distribution gap of the corresponding second sub-histogram. For example, the ratio adjustment unit (305) can detect changes in characteristic data based on the change (increase or decrease) in the ratio of the distribution gap of the corresponding first sub-histogram and the corresponding second sub-histogram in the corrected histogram relative to the ratio of the distribution gap of the first sub-histogram and the second sub-histogram in the original histogram.

[0056] The ratio control unit (306) can adjust the blending ratio based on the result of comparing characteristic data of the original histogram and the corrected histogram so that the characteristics of the corrected histogram maintain the characteristics of the original histogram. The ratio control unit (306) can generate a control signal to increase or decrease the ratio of the first smoothing algorithm or decrease or increase the ratio of the second smoothing algorithm in the first blending ratio.

[0057] The ratio adjustment unit (306) can detect the degree of skewness and clipping of the corresponding first sub-histogram and the corresponding second sub-histogram. Due to the skewness and clipping of the histogram, the shape of the corrected histogram may differ from the shape of the original histogram. The ratio adjustment unit (306) can adjust the stretching gain of the histogram based on the degree of skewness and clipping of the corresponding first sub-histogram and the corresponding second sub-histogram. The stretching gain may be a limit value for the minimum index and the maximum index on the horizontal axis of the histogram during histogram equalization. Increasing the stretching gain may be a process of changing the minimum index and the maximum index to increase the distribution gap during histogram equalization. Decreasing the stretching gain may be a process of changing the minimum index and the maximum index to decrease the distribution gap during histogram equalization.

[0058] By adjusting the stretching gain so that the shapes of the corresponding first subhistogram and the corresponding second subhistogram maintain the shapes of the first subhistogram and the second subhistogram, the occurrence of excessive slicing and / or clipping of the corresponding first subhistogram and the corresponding second subhistogram can be minimized.

[0059] Figure 3a is an example diagram showing the original thermal image and the original histogram of the original thermal image. Referring to Figure 3a, a cold area like the sky occupies a large part of the image, and the frequency of occurrence is concentrated in the low index area of ​​the histogram.

[0060] FIG. 3b is a diagram showing a corrected thermal image and a histogram thereof to which a first equalization algorithm is applied to the original thermal image according to one embodiment. Referring to FIG. 3b, as histogram equalization is performed based on the sky area (second area) which has a high frequency count, the building / road area (first area) other than the sky becomes saturated, the contrast is weakened, and the detail elements of the building / road area (first area) may be lost.

[0061] FIG. 3c is a diagram showing a corrected thermal image and a histogram thereof to which a second equalization algorithm is applied to the original thermal image according to one embodiment. Referring to FIG. 3c, as histogram equalization is performed based on the building / road area (first area), the contrast of the building / road area (first area) is well represented, but the sky area (second area) is degraded, and detail elements of the sky area may be lost.

[0062] FIG. 4 is a diagram illustrating an example of distinguishing subhistograms from a original histogram according to one embodiment. FIG. 4 is an example of distinguishing a second subhistogram (SH2) corresponding to a detail area (first area) and a first subhistogram (SH1) corresponding to an other area (second area) in an original thermal image. If the distribution gap (G) of the subhistogram is smaller than a threshold value, it is classified as the first subhistogram (SH1), and if the distribution gap (G) is greater than or equal to the threshold value, it can be classified as the second subhistogram (SH2). In FIG. 4, the sky area (second area) is classified as the first subhistogram (SH1), and the building / road area (first area) is classified as the second subhistogram (SH2). The distribution gap (CG) of the first subhistogram (SH1) is smaller than the distribution gap (DG) of the second subhistogram (SH2).

[0063] FIG. 5 is a diagram showing a first corrected thermal image and a corrected histogram for the first corrected thermal image according to one embodiment. Referring to FIG. 5, it can be seen that the first corrected thermal image, generated by applying a first smoothing algorithm and a second smoothing algorithm to the original thermal image according to the blending ratio, has an improved contrast ratio between the first region and the second region compared to the original thermal image of FIG. 3a.

[0064] FIG. 6 is a diagram illustrating a comparison of histograms for controlling the blending ratio according to one embodiment.

[0065] In the original histogram on the left, a first distribution gap (CG1), which is the difference between the minimum index (Cmin) and the maximum index (Cmax) on the horizontal axis of the first sub-histogram (SH1), and a second distribution gap (DG1), which is the difference between the minimum index (Dmin) and the maximum index (Dmax) on the horizontal axis of the second sub-histogram (SH2), can be detected, respectively. Additionally, in the corrected histogram on the right, a third distribution gap (CG2), which is the difference between the minimum index (Cmin') and the maximum index (Cmax') on the horizontal axis of the corresponding first sub-histogram (SH1'), and a corresponding fourth distribution gap (DG2), which is the difference between the minimum index (Dmin') and the maximum index (Dmax') on the horizontal axis of the corresponding second sub-histogram (SH2'), can be detected, respectively.

[0066] Referring to FIG. 6, in the original histogram, the second distribution gap (DG1) is larger than the first distribution gap (CG1). On the other hand, in the corrected histogram compared to the original histogram, the third distribution gap (CG2) is larger than the fourth distribution gap (DG2), so it can be seen that the ratio of the third distribution gap (CG2) to the fourth distribution gap (DG2) is reversed compared to the ratio of the first distribution gap (CG1) to the second distribution gap (DG1). In addition, it can be seen that the corresponding second sub-histogram (SH2') is clipped by shifting to the right, so the overall shape of the corresponding second sub-histogram (SH2') is different from the overall shape of the second sub-histogram (SH2).

[0067] The ratio control unit (306) can adjust the blending ratio so that the fourth distribution gap (DG2) increases more than the third distribution gap (CG2), and the ratio of the third distribution gap (CG2) to the fourth distribution gap (DG2) maintains the ratio of the first distribution gap (CG1) to the second distribution gap (DG1). For example, the ratio control unit (306) can increase the ratio of the second smoothing algorithm relative to the first smoothing algorithm so that the fourth distribution gap (DG2) increases more than the third distribution gap (CG2).

[0068] Additionally, the ratio control unit (306) can adjust the stretching gain to minimize the skewness of the corresponding second sub-histogram (SH2') so that the overall shape of the corresponding second sub-histogram (SH2') maintains the overall shape of the second sub-histogram (SH2). For example, the ratio control unit (306) can adjust the fourth distribution gap (DG2) by changing the minimum index (Dmin') and maximum index (Dmax') of the corresponding second sub-histogram (SH2').

[0069] The preprocessing unit (302) and the ratio adjustment unit (306) can repeatedly generate the first corrected thermal image and adjust the blending ratio based on the analysis of the corrected histogram until the characteristics of the corrected histogram become the characteristics of the original histogram.

[0070] An embodiment of the present invention can minimize the loss of detail elements in both the first and second regions by finding the optimal blending ratio of the first and second equalization algorithms through histogram analysis and equalizing the histogram of the original thermal image.

[0071] FIG. 7 is a diagram schematically illustrating a thermal image processing method according to one embodiment. The thermal image processing method of FIG. 7 can be performed by a processor (30) of a thermal image camera (1) shown in FIG. 1. Hereinafter, detailed descriptions of contents that overlap with those described with reference to FIG. 1 to 6 are omitted.

[0072] The processor (30) can analyze the original histogram of the original thermal image and determine the blending ratio, which is the degree of application of different first and second equalization algorithms, based on the analysis results. The first equalization algorithm is a method of equalizing the histogram based on areas with high frequency of occurrence in the histogram, and the second equalization algorithm is a method of equalizing the histogram based on areas with many detail elements.

[0073] The processor (30) can divide the original histogram into a second sub-histogram corresponding to a first region with many detail elements and a first sub-histogram corresponding to a second region other than the first region (S71). The processor (30) can divide the distribution gap into a first sub-histogram that is smaller than a threshold value and a second sub-histogram that is greater than or equal to a threshold value.

[0074] The processor (30) can determine the ratio of the first subhistogram and the second subhistogram as the blending ratio of the first smoothing algorithm and the second smoothing algorithm (S73).

[0075] The processor (30) can generate a first corrected thermal image by applying a first smoothing algorithm and a second smoothing algorithm to the original thermal image according to the blending ratio (S75).

[0076] The processor (30) can adjust the blending ratio by comparing the corrected histogram and the original histogram for the first corrected thermal image (S77). The processor (30) can detect a change between the characteristic data of the original histogram and the characteristic data of the corrected histogram. The processor (30) can detect a change between the characteristic data of the sub-histogram of the original histogram and the characteristic data of the corresponding sub-histogram. The processor (30) can adjust the blending ratio so that the characteristics of the corrected histogram maintain the characteristics of the original histogram. The processor (30) can adjust the blending ratio by analyzing the first corrected thermal image that is repeatedly generated until the characteristics of the corrected histogram become the characteristics of the original histogram.

[0077] Additionally, the processor (30) can adjust the stretching gain by comparing the corrected histogram and the original histogram. The processor (30) can detect the degree of histogram skewness and clipping in the corrected histogram and adjust the histogram stretching gain according to the degree of skewness or clipping. The processor (30) can adjust the histogram stretching gain so that the corrected histogram maintains the shape of the original histogram by minimizing the skewness and clipping in the corrected histogram.

[0078] The processor (30) can generate a second corrected thermal image by applying a first smoothing algorithm and a second smoothing algorithm to the original thermal image according to the finally adjusted blending ratio and stretching gain as a final corrected thermal image (S79).

[0079] FIG. 8 is a schematic diagram showing a thermal imaging camera according to one embodiment.

[0080] Referring to FIG. 8, the thermal imaging camera (2) may include a detector (100), a first signal processing unit (300a), a second signal processing unit (300b), an output unit (500), and a storage unit (700). The thermal imaging camera (2) shown in FIG. 8 can perform histogram equalization on the thermal image in the same way as the thermal imaging camera (1) shown in FIG. 1.

[0081] The detector (100) may be configured to correspond to the input section (10) of FIG. 1.

[0082] The first signal processing unit (300a) can generate a thermal image using pixel brightness values ​​input from the detector (100). The first signal processing unit (300a) can generate a corrected thermal image with improved contrast by performing histogram equalization and / or histogram stretching on the original thermal image data to adjust the distribution of pixel brightness values ​​of the original thermal image.

[0083] The second signal processing unit (300b) can analyze the histogram and determine the application ratio of a plurality of histogram equalization algorithms for generating a corrected thermal image. The second signal processing unit (300b) can perform additional image processing on the corrected thermal image on which histogram equalization has been performed.

[0084] As described with reference to FIG. 1, histogram equalization may be performed by a single processor, or as shown in FIG. 8, the first signal processing unit (300a) and the second signal processing unit (300b) may be implemented as separate processors to perform histogram equalization. In one embodiment, the first signal processing unit (300a) may be implemented as a field programmable gate array (FPGA), and the second signal processing unit (300b) may be implemented as an image signal processor (ISP).

[0085] The thermal image processed through the first signal processing unit (300a) and the second signal processing unit (300b) can be provided to the user through the output unit (500).

[0086] The storage unit (700) may store parameters required for signal processing of the first signal processing unit (300a) and the second signal processing unit (300b), the original thermal image, and the corrected thermal image.

[0087] A thermal image processing method for a thermal imaging camera according to an embodiment of the present invention can be implemented as computer-readable code on a computer-readable recording medium. A computer-readable recording medium includes all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Furthermore, the computer-readable recording medium may be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. Additionally, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers skilled in the art to which the present invention belongs.

[0088] Some aspects of the present invention have been described with reference to embodiments illustrated in the accompanying drawings, but this is merely illustrative and will be understood by those skilled in the art that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true scope of protection of the present invention should be determined only by the appended claims.

Claims

Claim 1 A thermal image processing method comprising: a step of determining a blending ratio of a first smoothing algorithm and a second smoothing algorithm different from the first smoothing algorithm based on the analysis results of a original histogram for an original thermal image; a step of generating a first corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the blending ratio; a step of adjusting the blending ratio by comparing the corrected histogram for the first corrected thermal image with the original histogram; and a step of generating a second corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the adjusted blending ratio. Claim 2 A thermal image processing method according to claim 1, wherein the blending ratio determination step comprises: a step of dividing the original histogram into a second sub-histogram corresponding to a first region with many detail elements in the original thermal image and a first sub-histogram corresponding to a second region other than the first region; and a step of determining the ratio of the first sub-histogram and the second sub-histogram as the blending ratio of the first smoothing algorithm and the second smoothing algorithm. Claim 3 A thermal image processing method according to claim 2, wherein the histogram classification step comprises: classifying into the first subhistogram if the distribution gap, which is the difference between the minimum index and the maximum index on the horizontal axis of the subhistogram, is smaller than a threshold value, and classifying into the second subhistogram if the distribution gap is greater than or equal to the threshold value. Claim 4 A thermal image processing method according to paragraph 2, wherein the ratio of the first subhistogram and the second subhistogram is the ratio of the sum of the occurrence frequencies of the first subhistogram to the sum of the occurrence frequencies of the second subhistogram. Claim 5 A thermal image processing method according to claim 2, wherein the blending ratio adjustment step comprises: a step of detecting second characteristic data of a corresponding first subhistogram corresponding to the first subhistogram and a corresponding second subhistogram corresponding to the second subhistogram in the correction histogram; and a step of adjusting the blending ratio by comparing the first characteristic data of the first subhistogram and the second subhistogram with the second characteristic data. Claim 6 A thermal image processing method according to claim 5, wherein the comparison of the first characteristic data and the second characteristic data comprises the comparison of the distribution gap of the first subhistogram and the distribution gap of the corresponding first subhistogram, and the comparison of the distribution gap of the second subhistogram and the distribution gap of the corresponding second subhistogram. Claim 7 A thermal image processing method according to claim 5, further comprising: a step of detecting the degree of skewness and clipping of the corresponding first sub-histogram and the corresponding second sub-histogram in the correction histogram; and a step of adjusting the gain of the corresponding first sub-histogram and the corresponding second sub-histogram according to the degree of skewness or clipping. Claim 8 A thermal image processing method according to claim 1, wherein the first equalization algorithm is a method of equalizing a histogram based on an area with a high frequency of occurrence in the histogram, and the second equalization algorithm is a method of equalizing a histogram based on an area with many detail elements. Claim 9 A thermal imaging camera comprising: a detector; and a processor that generates a thermal image using pixel brightness values ​​input from the detector; wherein the processor determines a blending ratio of a first smoothing algorithm and a second smoothing algorithm different from the first smoothing algorithm based on the analysis result of a original histogram for the original thermal image, generates a first corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the blending ratio, adjusts the blending ratio by comparing the corrected histogram for the first corrected thermal image with the original histogram, and generates a second corrected thermal image in which the first smoothing algorithm and the second smoothing algorithm are applied to the original thermal image according to the adjusted blending ratio. Claim 10 A thermal imaging camera according to claim 9, wherein the processor divides the original histogram into a second sub-histogram corresponding to a first region with many detail elements and a first sub-histogram corresponding to a second region other than the first region, and determines the ratio of the first sub-histogram and the second sub-histogram as the blending ratio of the first smoothing algorithm and the second smoothing algorithm. Claim 11 A thermal imaging camera according to claim 10, wherein the processor classifies into the first subhistogram if the distribution gap, which is the difference between the minimum index and the maximum index on the horizontal axis of the subhistogram, is smaller than the threshold value, and classifies into the second subhistogram if the distribution gap is greater than or equal to the threshold value. Claim 12 A thermal imaging camera, wherein in claim 10, the ratio of the first subhistogram and the second subhistogram is the ratio of the sum of the occurrence frequencies of the first subhistogram to the sum of the occurrence frequencies of the second subhistogram. Claim 13 A thermal imaging camera according to claim 10, wherein the processor detects second characteristic data of a corresponding first subhistogram corresponding to the first subhistogram and a corresponding second subhistogram corresponding to the second subhistogram in the correction histogram, and adjusts the blending ratio by comparing the first characteristic data of the first subhistogram and the second subhistogram with the second characteristic data. Claim 14 A thermal imaging camera according to claim 13, wherein the comparison of the first characteristic data and the second characteristic data comprises a comparison of the distribution gap of the first subhistogram and the distribution gap of the corresponding first subhistogram, and a comparison of the distribution gap of the second subhistogram and the distribution gap of the corresponding second subhistogram. Claim 15 A thermal imaging camera according to claim 13, wherein the processor detects the degree of skewness and clipping of the corresponding first sub-histogram and the corresponding second sub-histogram in the correction histogram, and adjusts the gain of the corresponding first sub-histogram and the corresponding second sub-histogram according to the degree of skewness or clipping. Claim 16 A thermal imaging camera according to claim 9, wherein the first equalization algorithm is a method of equalizing a histogram based on a region with a high frequency of occurrence in the histogram, and the second equalization algorithm is a method of equalizing a histogram based on a region with many detail elements.

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