CN211454662U - Intelligent image processing equipment based on deep learning - Google Patents
Intelligent image processing equipment based on deep learning Download PDFInfo
- Publication number
- CN211454662U CN211454662U CN202020319936.1U CN202020319936U CN211454662U CN 211454662 U CN211454662 U CN 211454662U CN 202020319936 U CN202020319936 U CN 202020319936U CN 211454662 U CN211454662 U CN 211454662U
- Authority
- CN
- China
- Prior art keywords
- image
- unit
- image processing
- deep learning
- data interface
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Abstract
The utility model discloses an intelligence image processing equipment based on degree of depth study belongs to degree of depth study technical field, its technical scheme main points include outer memory cell, data interface unit, interior memory cell, image preprocessing unit, image processing unit, image analysis unit and image output unit, outer memory cell is connected with data interface unit, data interface unit and image preprocessing unit electric connection, interior memory cell and image preprocessing unit electric connection, image preprocessing unit and image processing unit electric connection, image processing unit and image analysis unit electric connection, image analysis unit and image output unit electric connection. The utility model establishes a feedback mechanism by using the image analysis unit, thereby improving the intellectualization; by using the plurality of display screens, the images are displayed on different display screens according to different characteristics when being output, so that the image display device is beneficial to observing the same image from different characteristic contrasts, and the practicability is improved.
Description
Technical Field
The utility model belongs to the technical field of the degree of depth study, more specifically say, it relates to intelligent image processing equipment based on degree of depth study.
Background
The concept of deep learning stems from the study of artificial neural networks. A multi-layer perceptron with multiple hidden layers is a deep learning structure. Deep learning forms a more abstract class or feature of high-level representation properties by combining low-level features to discover a distributed feature representation of the data.
Image processing (image processing) techniques that analyze an image with a computer to achieve a desired result. Also known as image processing. Image processing generally refers to digital image processing. Digital images are large two-dimensional arrays of elements called pixels and values called gray-scale values, which are captured by industrial cameras, video cameras, scanners, etc. The image processing techniques generally include image compression, enhancement and restoration, matching, description and identification of 3 parts. Common systems include a durable system, a graphic intelligent system and the like, and are emerging technologies at present.
However, the present image processing apparatus based on deep learning lacks a feedback mechanism, and the output image is not compared according to different features, the utility model provides an intelligent image processing apparatus based on deep learning to the above-mentioned problem.
SUMMERY OF THE UTILITY MODEL
Not enough to prior art exists, the utility model aims to provide an intelligent image processing equipment based on degree of depth study, its advantage lies in can establishing feedback mechanism, strengthens the image characteristic, and observes same picture from different characteristic contrasts, has improved the practicality.
In order to achieve the above purpose, the utility model provides a following technical scheme: the intelligent image processing device based on deep learning comprises an external storage unit, a data interface unit, an internal storage unit, an image preprocessing unit, an image processing unit, an image analysis unit and an image output unit;
the image processing device comprises an external storage unit, an image preprocessing unit, an image analysis unit and an image output unit, wherein the external storage unit is connected with the data interface unit, the data interface unit is electrically connected with the image preprocessing unit, the internal storage unit is electrically connected with the image preprocessing unit, the image preprocessing unit is electrically connected with the image processing unit, the image processing unit is electrically connected with the image analysis unit, and the image analysis unit is electrically connected with the image output unit.
Preferably, the image preprocessing unit, the image processing unit and the image analysis unit are integrated on the same chip, so that external wiring is reduced, the reliability and flexibility of the equipment are improved, the power consumption is low, and the stability is good.
Preferably, the external storage unit comprises an optical disc, a U disk and a mobile hard disk, the internal storage unit comprises a RAM and a ROM, and the storage devices are diversified to provide convenience for image acquisition.
Preferably, the data interface unit includes a USB interface and an optical drive.
Preferably, the image output unit comprises a plurality of displays, each display is independent, the images can be displayed on different displays according to different characteristics of the images in the output process, workers can observe the same image according to different characteristics in a contrast mode, and the practicability is improved.
Preferably, the image analysis unit is provided with a feedback system, one or more judgment parameters are set in the image analysis unit, when the image of the image processing unit passes through the image analysis unit, the feedback system judges whether the image meets the parameters according to the judgment parameters, if yes, the image is output to the display, and if not, the image is fed back to the image processing unit.
To sum up, the utility model has the advantages of it is following:
1. by using the image analysis unit, whether the image meets the requirements or not can be judged in the image processing process, a closed-loop feedback system is established, and the intellectualization is improved;
2. by using the plurality of display screens, the images are displayed on different display screens according to different characteristics when being output, so that the image display device is beneficial to observing the same image from different characteristic contrasts, and the practicability is improved.
Drawings
FIG. 1 is a schematic diagram of the unit connection of the present invention;
Detailed Description
The technical solution of the present invention will be described clearly and completely with reference to the accompanying drawings, and obviously, the described embodiments are some, but not all embodiments of the present invention.
The components of the embodiments of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the accompanying drawings, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative work belong to the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The first embodiment is as follows:
the intelligent image processing device based on deep learning is shown in fig. 1 and comprises an external storage unit, a data interface unit, an internal storage unit, an image preprocessing unit, an image processing unit, an image analysis unit and an image output unit;
the external storage unit is connected with the data interface unit, the data interface unit is electrically connected with the image preprocessing unit, the internal storage unit is electrically connected with the image preprocessing unit, the image preprocessing unit is electrically connected with the image processing unit, the image processing unit is electrically connected with the image analysis unit, and the image analysis unit is electrically connected with the image output unit.
The image preprocessing unit, the image processing unit and the image analysis unit are integrated on the same chip, so that external wiring is reduced, the reliability and flexibility of the equipment are improved, the power consumption is low, and the stability is good.
The external storage unit comprises an optical disk, a U disk and a mobile hard disk, the internal storage unit comprises an RAM and an ROM, and the storage equipment is diversified to provide convenience for image acquisition.
The data interface unit comprises a USB interface and an optical drive.
The image output unit comprises a plurality of displays, each display is independent, images can be displayed on different displays according to different characteristics of the images in the output process, the image output unit is beneficial to workers to observe the same image according to different characteristic contrasts, and the practicability is improved.
The image processing unit is provided with an image analysis unit, the image analysis unit is provided with a feedback system, one or more judgment parameters are set in the image analysis unit, when the image of the image processing unit passes through the image analysis unit, the feedback system judges whether the image meets the parameters according to the judgment parameters, if yes, the image is output to the display, and if not, the image is fed back to the image processing unit.
Example two:
the working process of the utility model is as follows:
the method comprises the following steps: the external storage unit is connected with the data interface unit, and image judgment parameters are set in the image analysis unit;
step two: the image preprocessing unit retrieves image data from the external storage unit or the internal storage unit, and encodes, compresses and transforms the image without distortion.
Step three: and the image processing unit is used for further processing the image after the image preprocessing, and carrying out image segmentation, feature extraction, denoising and definition improvement on the image.
Step four: and a feedback mechanism in the image analysis unit judges whether the image meets the requirements, if so, the image is output to a display, and if not, the image is returned to the image processing unit.
Step five: and respectively outputting the images with different enhanced characteristics to different displays for comparative observation.
Example three:
as shown in fig. 1, the intelligent image processing apparatus based on deep learning:
in the image processing process, a digital image processing method is usually adopted, wherein the image transformation mainly converts the spatial domain processing of the image into transform domain processing by using the technologies such as Walsh transformation and the like, so that the calculation amount is reduced; the image coding and compression mainly reduce the bit number of the description picture and reduce the memory occupied by the picture; the image enhancement and restoration can strengthen the details, improve the definition and make the outline clear; and the image segmentation is used for extracting picture characteristics and further analyzing the image.
As shown in fig. 1, the unit reference models of the intelligent image processing apparatus based on deep learning are as follows:
the external storage unit usually adopts a U disk with a memory of 32G-128G, the RAM in the internal storage unit needs to be larger than 4G, and the ROM needs to be larger than 64G, so that the phenomenon of blockage caused by insufficient memory during image processing is prevented;
the data interface adopts a USB3.0 port and a COMBO optical drive, and the data aggregation transmission rate is improved.
The image preprocessing, the image processing and the image analysis are used as key parts of the image processing, a Cyclone 5 chip can be adopted, the chip contains an FPGA (field programmable gate array) and can be used for preprocessing pictures, such as simple image enhancement, and in addition, the chip also contains cortex 9, so that a more complex image algorithm can be realized, and the image quality is improved;
the image output unit can adopt an IPS display with the resolution ratio of more than 1920x1080 and the screen ratio of 4:3, the IPS display is excellent in visual angle and color reality performance, and image distortion can be avoided. The above description is only the preferred embodiment of the present invention, and is not intended to limit the present invention, any modification, equivalent replacement, or improvement made within the design concept of the present invention should be included within the protection scope of the present invention.
Claims (6)
1. Intelligent image processing equipment based on deep learning, including outer memory cell, data interface unit, interior memory cell, image preprocessing unit, image processing unit, image analysis unit and image output unit, its characterized in that:
the image processing device comprises an external storage unit, an image preprocessing unit, an image analysis unit and an image output unit, wherein the external storage unit is connected with the data interface unit, the data interface unit is electrically connected with the image preprocessing unit, the internal storage unit is electrically connected with the image preprocessing unit, the image preprocessing unit is electrically connected with the image processing unit, the image processing unit is electrically connected with the image analysis unit, and the image analysis unit is electrically connected with the image output unit.
2. The intelligent image processing device based on deep learning according to claim 1, wherein: the image preprocessing unit, the image processing unit and the image analysis unit are integrated on the same chip.
3. The intelligent image processing device based on deep learning according to claim 1, wherein: the external storage unit comprises an optical disk, a U disk and a mobile hard disk, and the internal storage unit comprises an RAM and an ROM.
4. The intelligent image processing device based on deep learning according to claim 1, wherein: the data interface unit comprises a USB interface and an optical drive.
5. The intelligent image processing device based on deep learning according to claim 1, wherein: the image output unit includes a plurality of displays.
6. The intelligent image processing device based on deep learning according to claim 1, wherein: the image analysis unit is provided with a feedback mechanism.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202020319936.1U CN211454662U (en) | 2020-03-16 | 2020-03-16 | Intelligent image processing equipment based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202020319936.1U CN211454662U (en) | 2020-03-16 | 2020-03-16 | Intelligent image processing equipment based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN211454662U true CN211454662U (en) | 2020-09-08 |
Family
ID=72299940
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202020319936.1U Expired - Fee Related CN211454662U (en) | 2020-03-16 | 2020-03-16 | Intelligent image processing equipment based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN211454662U (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117315214A (en) * | 2023-11-29 | 2023-12-29 | 深圳觉明人工智能有限公司 | Image processing device based on deep learning |
-
2020
- 2020-03-16 CN CN202020319936.1U patent/CN211454662U/en not_active Expired - Fee Related
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117315214A (en) * | 2023-11-29 | 2023-12-29 | 深圳觉明人工智能有限公司 | Image processing device based on deep learning |
| CN117315214B (en) * | 2023-11-29 | 2024-02-27 | 深圳觉明人工智能有限公司 | Image processing device based on deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112734646B (en) | Image super-resolution reconstruction method based on feature channel division | |
| WO2021159767A1 (en) | Medical image processing method, image processing method, and device | |
| Ding et al. | Efficient dark channel based image dehazing using quadtrees | |
| CN111489346B (en) | Full-reference image quality evaluation method and system | |
| CN108921942B (en) | Method and device for 2D (two-dimensional) conversion of image into 3D (three-dimensional) | |
| CN112396635B (en) | Multi-target detection method based on multiple devices in complex environment | |
| DE102016203881A1 (en) | Software interface tool for breast cancer investigation | |
| CN211454662U (en) | Intelligent image processing equipment based on deep learning | |
| CN111339989A (en) | Water body extraction method, device, equipment and storage medium | |
| CN112052730B (en) | 3D dynamic portrait identification monitoring equipment and method | |
| CN113554739A (en) | Relighting image generation method and device and electronic equipment | |
| Huang et al. | Light field image quality assessment: An overview | |
| CN104243970A (en) | 3D drawn image objective quality evaluation method based on stereoscopic vision attention mechanism and structural similarity | |
| CN107371026A (en) | A kind of video data Multiple Compression and reconstructing method | |
| CN101610422A (en) | Method for compressing three-dimensional image video sequence | |
| CN113076953A (en) | Black car detection method, system, device and storage medium | |
| CN111798497A (en) | Image processing method and device, electronic device and storage medium | |
| Li et al. | Image fusion algorithm based on contrast pyramid and application | |
| CN109583426A (en) | A method of according to image identification face | |
| CN114494427A (en) | Method, system and terminal for detecting illegal behavior of person standing under suspension arm | |
| CN109429054A (en) | VR method for compressing image and its device | |
| Biemond et al. | Image and video processing | |
| CN114332343A (en) | Image processing method, image processing apparatus, computer device, and storage medium | |
| CN111815547A (en) | Image processing method and device, electronic device and storage medium | |
| Scott et al. | Laplacian based image fusion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: Room C2209, Kechuang headquarters building, No. 320, pubin Road, Nanjing area, Jiangsu Free Trade Zone, Nanjing City, Jiangsu Province Patentee after: Nanjing Hexian Digital Technology Development Co.,Ltd. Address before: Room 12049, zone 1, building 1, No. 888, Huanhu West 2nd Road, Nanhui new town, Pudong New Area, Shanghai, 201306 Patentee before: Shanghai Hexian Network Technology Co.,Ltd. |
|
| CP03 | Change of name, title or address | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200908 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |