Image processing system

Abstract

The invention relates to an image processing system which comprises a processing judgment device and an image processing device. The processing mark is used for intelligently judging whether the image to be subjected to special effect identification is subjected to filter special effect processing for adjusting picture hue or not on the basis of the secondary system value of the standard pattern in the JPEG file format, the secondary system value of the reference pattern in the JPEG file format and the binary system value of the image to be subjected to special effect identification in the JPEG file format; and the mark reporting equipment is used for reporting the processing mark to a far-end regional chain server. The image processing system is intelligent in operation and simple and convenient to operate. Whether the image to be subjected to special effect identification is subjected to the filter special effect processing of adjusting the image hue or not can be intelligently judged based on the selected various visual information by adopting the Hough neural network model designed in a targeted structure, so that effective auxiliary information is provided for identifying the truth of the image to be subjected to special effect identification.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and more particularly, to an image processing system. Background Art

[0002] There are many types of image special effect processing, and filter special effects are one of the main types. Filters are mainly used to achieve various special effects of images, and they have a very magical effect in Photoshop. All filters are classified and placed in the menu in Photoshop, and only need to execute this command from the menu when using. The operation of the filter is very simple, but it is really difficult to use it just right. Filters usually need to be used in combination with channels, layers, etc. to achieve the best artistic effect. If you want to apply the filter to the most appropriate position at the most appropriate time, in addition to the usual artistic skills, it also requires the user's familiarity and control ability of the filter, and even requires rich imagination. In this way, the filter can be applied targeted and the artistic talent can be exerted.

[0003] In the prior art, it is impossible to perform filter special effect processing on the received picture to adjust the picture tone, resulting in difficulty in providing effective auxiliary information for identifying the authenticity of the received picture, and further affecting the accurate judgment of the picture content. Summary of the Invention

[0004] In order to solve the technical problems in the related field, the present invention provides an image processing system. By acquiring and storing a standard pattern and a reference pattern obtained after the standard pattern is subjected to filter special effect processing for adjusting the picture tone, and analyzing the binary value of the standard pattern in the JPEG file format and the binary value of the reference pattern in the JPEG file format, sufficient and reliable basic content is provided for the intelligent judgment of whether the image to be identified for special effects has been subjected to filter special effect processing for adjusting the picture tone. And the Hopfield neural network model is used to judge whether the image to be identified for special effects has been subjected to filter special effect processing for adjusting the picture tone based on the binary value of the standard pattern in the JPEG file format, the binary value of the reference pattern in the JPEG file format, and the binary value of the image to be identified for special effects in the JPEG file format, so as to provide effective auxiliary information for identifying the authenticity of the image to be identified for special effects. Among them, a Hopfield neural network model with a targeted structural design is introduced for the intelligent judgment of the filter special effect processing for adjusting the picture tone. Specifically, the Hopfield neural network is continuously learned multiple times to obtain the Hopfield neural network after multiple learning, and the Hopfield neural network after multiple learning is used as the Hopfield neural network model. The number of times of learning of the Hopfield neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects, thus ensuring the stability of the judgment result of the intelligent judgment of whether the image to be identified for special effects has been subjected to filter special effect processing for adjusting the picture tone.

[0005] According to the present invention, an image processing system is provided, and the system includes: A continuous learning mechanism for obtaining a Huffet neural network model. In the Huffet neural network model, the Huffet neural network is continuously learned multiple times to obtain the Huffet neural network after multiple learning, and the Huffet neural network after multiple learning is used as the Huffet neural network model; An information storage mechanism for obtaining and storing a standard pattern, a reference pattern obtained after the standard pattern is subjected to a filter effect process of adjusting the picture tone, and analyzing the binary value of the standard pattern in the JPEG file format and the binary value of the reference pattern in the JPEG file format; A content conversion mechanism for performing image coding format conversion on the image to be identified for special effects to obtain the binary value of the image to be identified for special effects in the JPEG file format; A processing and judgment device, which is respectively connected to the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to use the binary value of the standard pattern in the JPEG file format, the binary value of the reference pattern in the JPEG file format, and the binary value of the image to be identified for special effects in the JPEG file format as multiple input contents of the Huffet neural network model to execute the Huffet neural network model, and obtain a processing flag indicating whether the image to be identified for special effects has been subjected to a filter effect process of adjusting the picture tone output by the Huffet neural network model; A flag reporting device, which is connected to the processing and judgment device, and is used to report the processing flag indicating whether the image to be identified for special effects has been subjected to a filter effect process of adjusting the picture tone output by the Huffet neural network model to a remote blockchain server through a wireless communication link; Wherein, the continuous learning mechanism for obtaining a Huffet neural network model. In the Huffet neural network model, the Huffet neural network is continuously learned multiple times to obtain the Huffet neural network after multiple learning, and the Huffet neural network after multiple learning is used as the Huffet neural network model includes: the number of times of learning of the Huffet neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects.

[0006] Therefore, the present invention has at least the following three beneficial technical effects: First: Obtain and store a standard pattern, a reference pattern obtained after the standard pattern is subjected to a filter effect process of adjusting the picture tone, and analyze the binary value of the standard pattern in the JPEG file format and the binary value of the reference pattern in the JPEG file format, so as to provide sufficient and reliable basic content for the intelligent judgment of whether the image to be identified for special effects has been subjected to a filter effect process of adjusting the picture tone; Secondly: The Hoffit neural network model is used to judge whether the image to be identified for special effects has been processed by a filter special effect for adjusting the picture tone based on the binary values of the standard pattern in the JPEG file format, the binary values of the reference pattern in the JPEG file format, and the binary values of the image to be identified for special effects in the JPEG file format, so as to provide effective auxiliary information for the identification of the authenticity of the image to be identified for special effects; Finally: A Hoffit neural network model with a targeted structure design is introduced for the intelligent judgment of the filter special effect for adjusting the picture tone. Among them, the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning, and the Hoffit neural network after multiple learning is used as the Hoffit neural network model. The number of times of learning of the Hoffit neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects, thus ensuring the stability of the judgment result of the intelligent judgment on whether the image to be identified for special effects has been processed by the filter special effect for adjusting the picture tone.

[0007] The image processing system of the present invention operates intelligently and is easy to operate. Since it can use the Hoffit neural network model with a targeted structure design to intelligently judge whether the image to be identified for special effects has been processed by the filter special effect for adjusting the picture tone based on the selected visual information, it provides effective auxiliary information for the identification of the authenticity of the image to be identified for special effects. Specific Embodiment

[0008] The implementation scheme of the image processing system of the present invention will be described in detail below.

[0009] First Embodiment

[0010] The image processing system according to the first embodiment of the present invention includes: A continuous learning mechanism for obtaining a Hoffit neural network model, in which the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning, and the Hoffit neural network after multiple learning is used as the Hoffit neural network model; An information storage mechanism for obtaining and storing a standard pattern and a reference pattern obtained after the standard pattern is processed by a filter special effect for adjusting the picture tone, and analyzing the binary values of the standard pattern in the JPEG file format and the binary values of the reference pattern in the JPEG file format; Exemplarily, an information storage institution is used to obtain and store a standard pattern and a reference pattern obtained after the standard pattern is subjected to a filter effect process for adjusting the picture tone, and to analyze the binary value of the standard pattern in the JPEG file format and the binary value of the reference pattern in the JPEG file format, including: selecting to use a FLASH storage chip or an MMC storage chip to implement the information storage institution for obtaining and storing the standard pattern and the reference pattern obtained after the standard pattern is subjected to the filter effect process for adjusting the picture tone, and for analyzing the binary value of the standard pattern in the JPEG file format and the binary value of the reference pattern in the JPEG file format; A content conversion institution is used to perform image coding format conversion on the image to be identified for special effects to obtain the binary value of the image to be identified for special effects in the JPEG file format; A processing and judgment device is respectively connected to the continuous learning institution, the information storage institution, and the content conversion institution, and is used to use the binary value of the standard pattern in the JPEG file format, the binary value of the reference pattern in the JPEG file format, and the binary value of the image to be identified for special effects in the JPEG file format as multiple input contents of the Hoffit neural network model to execute the Hoffit neural network model, and obtain a processing flag indicating whether the image to be identified for special effects has been subjected to the filter effect process for adjusting the picture tone output by the Hoffit neural network model; A flag reporting device is connected to the processing and judgment device, and is used to report the processing flag indicating whether the image to be identified for special effects has been subjected to the filter effect process for adjusting the picture tone output by the Hoffit neural network model to a remote blockchain server through a wireless communication link; Among them, a continuous learning institution is used to obtain a Hoffit neural network model. In the Hoffit neural network model, the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning, and the Hoffit neural network after multiple learning is used as the Hoffit neural network model, including: the number of times of learning of the Hoffit neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects; Among them, a continuous learning institution is used to obtain a Hoffit neural network model. In the Hoffit neural network model, the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning, and the Hoffit neural network after multiple learning is used as the Hoffit neural network model further includes: using a numerical conversion function to represent the numerical conversion relationship that the number of times of learning of the Hoffit neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects.

[0011] Second Embodiment

[0012] The image processing system according to the second embodiment of the present invention may further include the following components: A parameter configuration device, which is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively; Among them, the parameter configuration device, which is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, includes: the parameter configuration device uses the IIC configuration bus to provide parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively; Among them, the parameter configuration device uses the IIC configuration bus to provide parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, includes: the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively have different IIC configuration addresses; Among them, the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively have different IIC configuration addresses, includes: the different IIC configuration addresses of the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism are in binary numerical representation mode; And among them, the different IIC configuration addresses of the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism are in binary numerical representation mode, includes: the byte lengths of the different IIC configuration addresses of the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism are equal.

[0013] The third embodiment

[0014] The image processing system according to the third embodiment of the present invention may further include the following components: A positioning operation device, which is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively; Among them, the positioning operation device is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, including: the positioning operation device uses the GPS positioning mechanism to provide GPS positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively; And among them, the positioning operation device is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, including: the positioning operation device uses the Beidou positioning mechanism to provide Beidou positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively.

[0015] In addition, in the image processing system, the continuous learning mechanism is used to obtain the Hoffit neural network model. In the Hoffit neural network model, the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning, and the Hoffit neural network after multiple learning is used as the Hoffit neural network model. It also includes: in the numerical conversion function, the number of times of learning of the Hoffit neural network is the output content, and the signal-to-noise ratio of the image to be specially identified is the input content.

[0016] Although the specific embodiments of the present invention have been described in detail, the width and scope of the present invention should not be limited by the above exemplary embodiments, but should be defined only by the following claims and their equivalents. All changes and modifications falling within the spirit of the present invention are expected to be protected.

Claims

1. An image processing system, characterized in that, The system includes: A continuous learning mechanism for obtaining a Hoffit neural network model. In the Hoffit neural network model, the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning sessions. Taking the Hoffit neural network after multiple learning sessions as the Hoffit neural network model includes: the number of times of learning of the Hoffit neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects; An information storage mechanism for obtaining and storing a standard pattern, a reference pattern obtained after the standard pattern is subjected to filter special effect processing for adjusting the picture tone, and parsing the binary value of the standard pattern in the JPEG file format and the binary value of the reference pattern in the JPEG file format; A content conversion mechanism for performing image encoding format conversion on the image to be identified for special effects to obtain the binary value of the image to be identified for special effects in the JPEG file format; A processing and judgment device, connected to the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, for using the binary value of the standard pattern in the JPEG file format, the binary value of the reference pattern in the JPEG file format, and the binary value of the image to be identified for special effects in the JPEG file format as multiple input contents of the Hoffit neural network model to execute the Hoffit neural network model, and obtaining a processing flag indicating whether the image to be identified for special effects has been subjected to filter special effect processing for adjusting the picture tone output by the Hoffit neural network model; A flag reporting device, connected to the processing and judgment device, for reporting the processing flag indicating whether the image to be identified for special effects has been subjected to filter special effect processing for adjusting the picture tone output by the Hoffit neural network model to a remote blockchain server through a wireless communication link.

2. The image processing system according to claim 1, wherein: A continuous learning mechanism for obtaining a Hoffit neural network model. In the Hoffit neural network model, the Hoffit neural network is continuously learned multiple times to obtain the Hoffit neural network after multiple learning sessions. Taking the Hoffit neural network after multiple learning sessions as the Hoffit neural network model further includes: using a numerical conversion function to represent the numerical conversion relationship in which the number of times of learning of the Hoffit neural network is inversely proportional to the signal-to-noise ratio of the image to be identified for special effects.

3. The image processing system according to claim 2, wherein The system further includes: A parameter configuration device, connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, for providing respective parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism; Among them, the parameter configuration device, connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, for providing respective parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism includes: the parameter configuration device uses the IIC configuration bus to provide respective parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism.

4. The image processing system according to claim 3, wherein: The parameter configuration device uses the IIC configuration bus to provide parameter configuration services required by the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, including: the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism have different IIC configuration addresses respectively.

5. The image processing system according to claim 4, wherein: The processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism having different IIC configuration addresses respectively include: the different IIC configuration addresses of the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism are in binary numerical representation mode.

6. The image processing system according to claim 5, wherein: The different IIC configuration addresses of the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism being in binary numerical representation mode include: the byte lengths of the different IIC configuration addresses of the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism are equal.

7. The image processing system according to claim 2, wherein The system further includes: The positioning operation device, which is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively.

8. The image processing system according to claim 7, wherein: The positioning operation device, which is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, includes: the positioning operation device uses the GPS positioning mechanism to provide GPS positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively.

9. The image processing system according to claim 7, wherein: The positioning operation device, which is respectively connected to the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism, and is used to provide positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively, includes: the positioning operation device uses the Beidou positioning mechanism to provide Beidou positioning services for the processing and judgment device, the continuous learning mechanism, the information storage mechanism, and the content conversion mechanism respectively.