Exposure state determination and exposure method for endoscope, medium, and computing device

Abstract

The present disclosure relates to an exposure state determination method and an exposure method of an endoscope, a medium, and a computing device, wherein the exposure state determination method of the endoscope comprises: acquiring a target image by using the endoscope; performing histogram statistics on the brightness of the target image to obtain a brightness histogram; performing average estimation on the brightness histogram to obtain a reference brightness value, and determining whether the reference brightness value belongs to a first stable interval; performing information entropy estimation on the brightness histogram to obtain an information entropy value, and determining whether the information entropy value belongs to a second stable interval; and in the case that the reference brightness value belongs to the first stable interval and / or the information entropy value belongs to the second stable interval, determining that the exposure state of the endoscope is a stable state. The exposure state determination method improves the scene applicability of the endoscope, so that the endoscope can obtain more rich image details in the stable exposure state.

Description

Technical Field

[0001] This disclosure generally relates to the field of digital image processing technology, and more specifically, to an endoscope exposure state determination and exposure method, medium, and computing device. Background Technology

[0002] Information entropy estimation and histogram estimation of images are both well-established algorithms. However, the single-estimation method system in traditional algorithms cannot fully cover all scenarios. For example, the overall brightness of an endoscope is high, but this is actually due to overexposure in a part of the lens being too close to the inner wall of the cavity in a good scene. This part is negligible, and the information entropy and histogram estimation value are high in this case. Stable exposure in this scene can obtain good image details. However, the single-estimation method system in traditional algorithms cannot stably expose in this scene, so it cannot effectively handle the above situation and it is difficult to obtain good image details. Summary of the Invention

[0003] This disclosure provides an endoscope exposure state determination and exposure method, medium, and computing device. By using information entropy estimation and histogram estimation for dual-system estimation, the exposure state of the endoscope can be determined based on the dual-system estimation results, improving the endoscope's applicability to various scenarios and enabling the endoscope to obtain richer image details under stable exposure conditions.

[0004] In one general aspect, a method for determining the exposure state of an endoscope is provided, comprising: acquiring a target image using an endoscope; performing histogram statistics on the brightness of the target image to obtain a brightness histogram; performing average estimation on the brightness histogram to obtain a reference brightness value, and determining whether the reference brightness value belongs to a first stable interval; performing information entropy estimation on the brightness histogram to obtain an information entropy value, and determining whether the information entropy value belongs to a second stable interval; and determining the exposure state of the endoscope as a stable state if the reference brightness value belongs to the first stable interval and / or the information entropy value belongs to the second stable interval.

[0005] Optionally, it further includes: if the reference brightness value does not belong to the first stable range and the information entropy value does not belong to the second stable range, determining the exposure state of the endoscope as a state to be adjusted.

[0006] Optionally, performing histogram statistics on the brightness of the target image includes: dividing the target image into a preset number of block images; determining the effective statistical region of the target image in the preset number of block images; and performing histogram statistics on the brightness of the effective statistical region.

[0007] Optionally, determining the effective statistical region of the target image among the preset number of block images includes: determining the effective statistical region of the target image among the preset number of block images based on a pre-determined two-dimensional array, wherein the two-dimensional array is pre-determined based on the usage scenario of the endoscope, and the two-dimensional array includes a preset number of logical elements, each logical element corresponding to a block image, wherein the logical element is used to determine whether the corresponding block image belongs to the effective statistical region.

[0008] Optionally, the logical element includes 1 or 0, wherein determining the effective statistical region of the target image among the preset number of block images based on a predetermined two-dimensional array includes: for any block image, if the logical element corresponding to the block image is 1, determining that the block image belongs to the effective statistical region; if the logical element corresponding to the block image is 0, determining that the block image does not belong to the effective statistical region.

[0009] Optionally, determining whether the reference brightness value belongs to the first stable interval includes: determining the difference between the reference brightness value and a predetermined target brightness value, and comparing the absolute value of the difference with a first threshold; if the absolute value is less than or equal to the first threshold, determining that the reference brightness value belongs to the first stable interval; if the absolute value is greater than the first threshold, determining that the reference brightness value does not belong to the first stable interval.

[0010] Optionally, determining whether the information entropy value belongs to the second stable interval includes: determining the ratio between the information entropy value and a predetermined maximum information entropy value, and comparing the ratio with a second threshold; if the ratio is greater than or equal to the second threshold, determining that the information entropy value belongs to the second stable interval; if the ratio is less than the second threshold, determining that the information entropy value does not belong to the second stable interval.

[0011] In another general aspect, an endoscope exposure method is provided, comprising: determining the exposure state of the endoscope using the endoscope exposure state determination method described above; and, if the exposure state of the endoscope is stable, performing exposure based on the exposure parameters of the endoscope in the stable state.

[0012] In another general aspect, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the endoscope exposure state determination method or the endoscope exposure method as described above.

[0013] In another general aspect, a computing device is provided, the computing device comprising: a processor; and a memory storing a computer program, which, when executed by the processor, implements the endoscope exposure state determination method or the endoscope exposure method as described above.

[0014] The endoscope exposure state determination and exposure method, medium, and computing device according to the embodiments of this disclosure utilize information entropy estimation and histogram estimation for dual-system estimation, which can effectively cover more endoscope application scenarios with rich details. This avoids the problem that the single estimation method system in traditional algorithms cannot stably expose in some scenarios with rich details, improves the scenario applicability of the endoscope, and enables the endoscope to obtain richer image details under stable exposure conditions.

[0015] Other aspects and / or advantages of the general concept of this disclosure will be set forth in part in the description which follows, and in part will be clear from the description or may be learned by practice of the general concept of this disclosure. Attached Figure Description

[0016] The above and other objects and features of the embodiments of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings illustrating the embodiments, wherein:

[0017] Figure 1 This is a flowchart illustrating a method for determining the exposure state of an endoscope according to an embodiment of the present disclosure;

[0018] Figure 2 This is a flowchart illustrating an exposure method for an endoscope according to an embodiment of the present disclosure;

[0019] Figure 3 This is a block diagram illustrating a computing device according to an embodiment of the present disclosure. Detailed Implementation

[0020] The following detailed embodiments are provided to aid the reader in gaining a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear upon understanding this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0021] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, apparatus, and / or systems described herein, which will become clear upon understanding the disclosure of this application.

[0022] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0023] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this disclosure.

[0024] The following will refer to Figures 1 to 3 The present disclosure provides a detailed description of the endoscope's exposure state determination and exposure method, medium, and computing device according to embodiments of the present disclosure.

[0025] Figure 1 This illustrates a method for determining the exposure state of an endoscope according to an embodiment of the present disclosure.

[0026] Reference Figure 1 In step S101, an endoscope can be used to acquire a target image. As an example, the endoscope can be a disposable bronchoscope, and in the corresponding usage scenario, the main observation area is the distal end, but this disclosure is not limited to this.

[0027] Next, in step S102, histogram statistics can be performed on the brightness of the target image to obtain a brightness histogram. Here, during histogram statistics, the target image can first be divided into ROI (region of interest). For example, the target image can be divided into a preset number of block images. Then, the effective statistical region of the target image is determined within the preset number of block images. Then, histogram statistics are performed on the brightness of the effective statistical region. Further, the preset number of block images can be determined by those skilled in the art based on the actual situation; for example, the target image can be divided into 64*64 block images, however, this disclosure is not limited to this. By determining the effective statistical region in the target image, the reliability of the brightness histogram statistics is improved, and image noise is avoided.

[0028] According to embodiments of this disclosure, a valid statistical region of a target image can be determined from a predetermined number of block images based on a pre-determined two-dimensional array. Here, the two-dimensional array can be pre-determined based on the endoscope's usage scenario, and the two-dimensional array can include a predetermined number of logical elements, each corresponding to a block image. The logical elements are used to determine whether the corresponding block image belongs to a valid statistical region. For example, when the target image is divided into 64*64 block images, a 64*64 two-dimensional array can be pre-determined based on the endoscope's usage scenario, thereby using the two-dimensional array as a logical switch to select the parts of interest in the current endoscope usage scenario as valid statistical regions. By utilizing a pre-determined two-dimensional array to determine the valid statistical region of the target image, the efficiency of empirical processing of valid statistical regions is improved.

[0029] According to embodiments of this disclosure, logical elements may include 1 or 0, meaning the two-dimensional array is composed of either 1 or 0 logical elements. In one possible implementation, for any given block image, if the logical element corresponding to the block image is 1, it can be determined that the block image belongs to a valid statistical region; if the logical element corresponding to the block image is 0, it can be determined that the block image does not belong to a valid statistical region. However, this disclosure is not limited to this. By using either 1 or 0 logical elements, the intuitiveness of the two-dimensional array is increased, facilitating the labeling of the two-dimensional array and subsequent processing using it.

[0030] Next, in step S103, the brightness histogram can be averaged to obtain a reference brightness value, and it can be determined whether the reference brightness value belongs to the first stable interval. Here, the difference between the reference brightness value and a predetermined target brightness value can be determined, and the absolute value of the difference can be compared with a first threshold. If the absolute value is less than or equal to the first threshold, the reference brightness value is determined to belong to the first stable interval; if the absolute value is greater than the first threshold, the reference brightness value is determined not to belong to the first stable interval. As an example, the target brightness value can be 128, and the first threshold can be 5, so the first stable interval is [123, 133]. In other words, when the reference brightness value is within the range of [123, 133], it can be considered that the reference brightness value belongs to the first stable interval; however, this disclosure is not limited to this. Determining whether the reference brightness value belongs to the first stable interval by the difference between the reference brightness value and the target brightness value is simple, easy to implement, and improves the discrimination efficiency.

[0031] Next, in step S104, information entropy estimation can be performed on the brightness histogram to obtain an information entropy value, and it can be determined whether the information entropy value belongs to the second stable interval. Here, the ratio between the information entropy value and a predetermined maximum information entropy value can be determined, and the ratio can be compared with a second threshold. If the ratio is greater than or equal to the second threshold, the information entropy value is determined to belong to the second stable interval; if the ratio is less than the second threshold, the information entropy value is determined not to belong to the second stable interval. As an example, the maximum information entropy value can be 12, and the second threshold can be 0.95, so the second stable interval is [11.4, 12]. In other words, when the information entropy value is within the range of [11.4, 12], it can be considered that the information entropy value belongs to the second stable interval; however, this disclosure is not limited to this. Determining whether the information entropy value belongs to the second stable interval by using the ratio between the information entropy value and the maximum information entropy value improves the discrimination efficiency and is reasonable for information entropy.

[0032] It should be understood that the values ​​of the target brightness value, the maximum information entropy value, the first threshold and the second threshold mentioned above are merely illustrative examples. The specific values ​​can be determined by those skilled in the art based on the actual situation. For example, the target brightness value, the maximum information entropy value, the first threshold and the second threshold can be calibrated by means of acquiring target images through simulation experiments. This disclosure does not impose any restrictions on this.

[0033] Next, in step S105, the endoscope's exposure state can be determined to be stable if the reference brightness value belongs to the first stable range and / or the information entropy value belongs to the second stable range. That is, when either the reference brightness value or the information entropy value is within a stable range, the endoscope's exposure state can be considered stable, and there is no need to adjust the exposure parameters to obtain richer image details. Alternatively, if the reference brightness value does not belong to the first stable range and the information entropy value does not belong to the second stable range, the endoscope's exposure state can be determined to be in a state requiring adjustment, and then the exposure parameters can be adjusted so that either the reference brightness value or the information entropy value eventually falls within a stable range.

[0034] The endoscope exposure state determination method according to the embodiments of this disclosure uses information entropy estimation and histogram estimation for dual-system estimation, which can effectively cover more endoscope application scenarios with rich details. It avoids the problem that the single estimation method system in traditional algorithms cannot stably expose in some scenarios with rich details, improves the scenario applicability of endoscopes, and enables endoscopes to obtain richer image details under stable exposure.

[0035] Figure 2 This is a flowchart illustrating an exposure method of an endoscope according to an embodiment of the present disclosure.

[0036] Reference Figure 2In step S201, the exposure state of the endoscope can be determined using the endoscope exposure state determination method according to the embodiments of the present disclosure.

[0037] Next, in step S202, exposure can be performed based on the exposure parameters of the endoscope in a stable state, provided that the exposure state of the endoscope is stable.

[0038] Figure 3 This is a block diagram illustrating a computing device according to an embodiment of the present disclosure.

[0039] Reference Figure 3 The computing device 300 according to embodiments of the present disclosure may include a processor 310 and a memory 320. The processor 310 may include (but is not limited to) a central processing unit (CPU), a digital signal processor (DSP), a microcomputer, a field-programmable gate array (FPGA), a system-on-a-chip (SoC), a microprocessor, an application-specific integrated circuit (ASIC), etc. The memory 320 stores computer programs to be executed by the processor 310. The memory 320 includes high-speed random access memory and / or a non-volatile computer-readable storage medium. When the processor 310 executes the computer program stored in the memory 320, the endoscope exposure state determination method or the endoscope exposure method described above can be implemented.

[0040] The endoscope exposure state determination method or endoscope exposure method according to embodiments of this disclosure can be programmed into a computer program and stored on a computer-readable storage medium. When the computer program is executed by a processor, the endoscope exposure state determination method or endoscope exposure method as described above can be implemented. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disc storage, hard disk drive (HDD), solid-state drive (SSD), card storage (such as multimedia cards, secure digital (SD) cards, or ultra-fast digital (XD) cards), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system, such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner through one or more processors or computers.

[0041] The endoscope exposure state determination and exposure method, medium, and computing device according to the embodiments of this disclosure utilize information entropy estimation and histogram estimation for dual-system estimation, which can effectively cover more endoscope application scenarios with rich details. This avoids the problem that the single estimation method system in traditional algorithms cannot stably expose in some scenarios with rich details, improves the scenario applicability of the endoscope, and enables the endoscope to obtain richer image details under stable exposure conditions.

[0042] While some embodiments of this disclosure have been shown and described, those skilled in the art will understand that modifications may be made to these embodiments without departing from the principles and spirit of this disclosure, which are defined by the claims and their equivalents.

Claims

1. A method for determining the exposure state of an endoscope, characterized in that, include: Acquire target images using an endoscope; Histogram statistics are performed on the brightness of the target image to obtain a brightness histogram; The brightness histogram is averaged to obtain a reference brightness value, and it is determined whether the reference brightness value belongs to the first stable interval. Information entropy is estimated from the brightness histogram to obtain the information entropy value, and it is determined whether the information entropy value belongs to the second stable interval. The exposure state of the endoscope is determined to be stable when any of the following conditions are met: Condition 1: The reference brightness value belongs to the first stable range; Condition 2: The information entropy value belongs to the second stable interval; Specifically, if only condition two is met but condition one is not met, the exposure state of the endoscope is still determined to be stable even if the reference brightness value deviates from the first stable range.

2. The exposure state determination method as described in claim 1, characterized in that, Also includes: If the reference brightness value does not belong to the first stable range and the information entropy value does not belong to the second stable range, the exposure state of the endoscope is determined to be in a state to be adjusted.

3. The exposure state determination method as described in claim 1, characterized in that, Histogram statistics are performed on the brightness of the target image, including: The target image is divided into a predetermined number of block images; Determine the effective statistical region of the target image from the preset number of block images; Histogram statistics are performed on the brightness of the effective statistical region.

4. The exposure state determination method as described in claim 3, characterized in that, Determining the effective statistical region of the target image from the preset number of block images includes: Based on a predetermined two-dimensional array, the effective statistical region of the target image is determined from the predetermined number of block images. The two-dimensional array is predetermined based on the usage scenario of the endoscope, and the two-dimensional array includes a preset number of logical elements, each of which corresponds to a block image. The logical elements are used to determine whether the corresponding block image belongs to the valid statistical region.

5. The exposure state determination method as described in claim 4, characterized in that, The logical elements include 1 or 0, wherein, based on a predetermined two-dimensional array, determining the effective statistical region of the target image among the predetermined number of block images includes: For any given block image, if the logical element corresponding to the block image is 1, then the block image is determined to belong to the valid statistical region. If the logical element corresponding to the block image is 0, it is determined that the block image does not belong to the valid statistical region.

6. The exposure state determination method as described in claim 1, characterized in that, Determining whether the reference brightness value belongs to the first stable range includes: Determine the difference between the reference brightness value and the predetermined target brightness value, and compare the absolute value of the difference with a first threshold. If the absolute value is less than or equal to the first threshold, the reference brightness value is determined to belong to the first stable range. If the absolute value is greater than the first threshold, it is determined that the reference brightness value does not belong to the first stable range.

7. The exposure state determination method as described in claim 1, characterized in that, Determining whether the information entropy value belongs to the second stable interval includes: Determine the ratio between the information entropy value and a predetermined maximum information entropy value, and compare the ratio with a second threshold; If the ratio is greater than or equal to the second threshold, the information entropy value is determined to belong to the second stable interval. If the ratio is less than the second threshold, it is determined that the information entropy value does not belong to the second stable interval.

8. An exposure method for an endoscope, characterized in that, include: The exposure state of the endoscope is determined using the endoscope exposure state determination method as described in any one of claims 1 to 7. When the endoscope is in a stable exposure state, the exposure is performed based on the exposure parameters of the endoscope in the stable state.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the endoscope exposure state determination method as described in any one of claims 1 to 7 or the endoscope exposure method as described in claim 8.

10. A computing device, characterized in that, The computing device includes: processor; and The memory stores a computer program that, when executed by a processor, implements the endoscope exposure state determination method as described in any one of claims 1 to 7 or the endoscope exposure method as described in claim 8.

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