CN115330626A - Picture transformation method and device based on mesh grid network decomposition - Google Patents
Picture transformation method and device based on mesh grid network decomposition Download PDFInfo
- Publication number
- CN115330626A CN115330626A CN202210990442.XA CN202210990442A CN115330626A CN 115330626 A CN115330626 A CN 115330626A CN 202210990442 A CN202210990442 A CN 202210990442A CN 115330626 A CN115330626 A CN 115330626A
- Authority
- CN
- China
- Prior art keywords
- image data
- optimization
- factor
- mesh grid
- correction operation
- 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.)
- Pending
Links
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 44
- 238000011426 transformation method Methods 0.000 title claims abstract description 21
- 238000005457 optimization Methods 0.000 claims abstract description 70
- 238000012937 correction Methods 0.000 claims abstract description 37
- 238000000034 method Methods 0.000 claims abstract description 32
- 230000009466 transformation Effects 0.000 claims abstract description 6
- 238000012163 sequencing technique Methods 0.000 claims description 11
- 230000000694 effects Effects 0.000 abstract description 11
- 230000009467 reduction Effects 0.000 abstract description 8
- 238000012545 processing Methods 0.000 description 21
- 238000004891 communication Methods 0.000 description 15
- 230000008878 coupling Effects 0.000 description 6
- 238000010168 coupling process Methods 0.000 description 6
- 238000005859 coupling reaction Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000012805 post-processing Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000009877 rendering Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Facsimile Image Signal Circuits (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a picture transformation method and a picture transformation device based on mesh grid network decomposition. Wherein, the method comprises the following steps: collecting original image information from high-precision camera equipment; decomposing the original image information by a mesh grid decomposition algorithm to obtain grid image data; performing anti-aliasing correction operation and gray level optimization operation on the gridding image data to obtain smooth image data; and uploading the smooth image data to a remote server. The invention solves the technical problems of incomplete image optimization, poor optimization effect and poor optimization efficiency caused by the fact that the acquired image optimization transformation method in the prior art only carries out noise reduction or point location optimization on original image data and even directly carries out operations such as restoration, gray scale resistance and the like on original pixels.
Description
Technical Field
The invention relates to the field of picture decomposition processing, in particular to a picture transformation method and device based on mesh grid network decomposition.
Background
Along with the continuous development of intelligent science and technology, people use intelligent equipment more and more among life, work, the study, use intelligent science and technology means, improved the quality of people's life, increased the efficiency of people's study and work.
At present, when image processing is performed, different image data is collected from each high-precision camera device, such as a hundred million-level camera device, and the image data is optimized to obtain data which can be used for image recognition and image retrieval. However, in the method for optimizing, processing and transforming the collected image in the prior art, the original image data is only subjected to noise reduction or point location optimization, and even the original pixels are directly subjected to operations such as restoration, gray scale resistance and the like, so that the image optimization is incomplete, the optimization effect is poor, and the optimization efficiency is poor.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a picture transformation method and a picture transformation device based on mesh network decomposition, which at least solve the technical problems that in the prior art, the collected image optimization transformation method only carries out noise reduction or point location optimization on original image data, and even directly carries out operations such as restoration, gray scale resistance and the like on original pixels, so that the image optimization is incomplete, the optimization effect is poor, and the optimization efficiency is poor.
According to an aspect of an embodiment of the present invention, there is provided a picture transformation method based on mesh grid network decomposition, including: collecting original image information from high-precision camera equipment; decomposing the original image information by a mesh grid decomposition algorithm to obtain grid image data; performing anti-sawtooth correction operation and gray level optimization operation on the gridding image data to obtain smooth image data; and uploading the smooth image data to a remote server.
Optionally, decomposing the original image information by using a mesh grid decomposition algorithm to obtain the gridded image data includes: acquiring a mesh grid network subentry factor and a weight factor; calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor.
Optionally, the antialiasing correction operation includes: FXAA correction operation, MSAA correction operation.
Optionally, before uploading the smoothed image data to a remote server, the method further includes: and sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
According to another aspect of the embodiments of the present invention, there is also provided a picture transformation apparatus based on mesh grid network decomposition, including: the acquisition module is used for acquiring original image information from the high-precision camera shooting equipment; the decomposition module is used for decomposing the original image information through a mesh grid decomposition algorithm to obtain grid image data; the optimization module is used for performing anti-aliasing correction operation and gray level optimization operation on the gridded image data to obtain smooth image data; and the uploading module is used for uploading the smooth image data to a remote server.
Optionally, the decomposition module includes: the acquiring unit is used for acquiring the item factors and the weight factors of the mesh grid network; a calculating unit, configured to calculate the gridding image data according to the polynomial factor and the weighting factor, where a formula for calculating the gridding image data includes:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor.
Optionally, the antialiasing correction operation comprises: FXAA correction operation, MSAA correction operation.
Optionally, the apparatus further comprises: and the sequencing module is used for sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided, where the non-volatile storage medium includes a stored program, and the program controls, when running, a device in which the non-volatile storage medium is located to execute a screen transformation method based on mesh grid network decomposition.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute a picture transformation method based on mesh grid network decomposition.
In the embodiment of the invention, the original image information is collected from high-precision camera equipment; decomposing the original image information by a mesh grid decomposition algorithm to obtain grid image data; performing anti-aliasing correction operation and gray level optimization operation on the gridding image data to obtain smooth image data; the smooth image data is uploaded to a remote server, and the technical problems that in the prior art, the image optimization is incomplete, the optimization effect is poor and the optimization efficiency is poor due to the fact that the acquired image optimization transformation method only carries out noise reduction or point location optimization on the original image data, and even directly carries out operations such as restoration and gray scale resistance on the original pixels.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a picture transformation method based on mesh grid network decomposition according to an embodiment of the present invention;
fig. 2 is a block diagram of a picture transformation apparatus based on mesh grid network decomposition according to an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing a method according to the present invention, according to an embodiment of the present invention;
fig. 4 is a memory unit for holding or carrying program code implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, there is provided a method embodiment of a screen transformation method based on mesh grid network decomposition, it should be noted that the steps shown in the flowchart of the figure may be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.
Example one
Fig. 1 is a flowchart of a picture transformation method based on mesh grid network decomposition according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
and step S102, acquiring original image information from the high-precision camera equipment.
Specifically, in order to solve the technical problems that the optimization of the image is incomplete, the optimization effect is poor, and the optimization efficiency is poor due to the fact that the collected image optimization transformation method in the prior art only performs noise reduction or point location optimization on the original image data, even directly performs operations such as restoration and gray scale resistance on the original pixels, and overcome related technical defects, the method and the device firstly need to collect and collect original image information needing picture change and optimization from high-precision camera equipment, store the original image, and facilitate subsequent decomposition of the original image information for optimization.
And step S104, decomposing the original image information through a mesh grid decomposition algorithm to obtain grid image data.
Optionally, decomposing the original image information by using a mesh grid decomposition algorithm to obtain grid image data includes: acquiring a mesh grid network subentry factor and a weight factor; calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor.
Specifically, after the original image information is acquired, in order to perform image segmentation and image decomposition by using the algorithm of the mesh grid network, so as to optimize and correct the decomposed images one by one, the method and the device need to acquire the mesh grid network subentry factor and the weighting factor; calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weight factor. That is, in order to obtain an aggregate of the gridded image data, when both n and i specify parameters, it is necessary to obtain gridded image data suitable for optimization by including the polynomial factor and the weight factor in the range of calculating the gridded image data.
And step S106, performing anti-aliasing correction operation and gray optimization operation on the gridded image data to obtain smooth image data.
Specifically, in order to perform an optimization operation on an image processed through a mesh network, the mesh image data may be subjected to an antialiasing correction operation and a grayscale optimization operation, where the antialiasing optimization operation may optimize and smooth pixels or singular points in each image data in the mesh network, and the grayscale optimization operation may perform brightness correction and adjustment on each image data in the mesh image data, so as to obtain smooth image data that can be uploaded and used for model construction.
Optionally, the antialiasing correction operation comprises: FXAA correction operation, MSAA correction operation.
In particular, the above FXAA is a high performance approximation of the MSAA effect. It is a one-way pixel shader, running at the post-processing stage of the target game rendering pipeline MLAA, but unlike the latter, which uses directcompute, it is simply a simple post-processing shader, not relying on any GPU computing API. Thus, the fxaa technology has no special requirements for graphics cards, and is fully compatible with different graphics cards for NVIDIA and AMD, and DX9, DX10, and DX 11.
And step S108, uploading the smooth image data to a remote server.
Specifically, after the smooth image data is acquired, all the smooth image data needs to be uploaded to a remote server, so that subsequent model training or image processing on the smooth image data is facilitated.
Optionally, before uploading the smoothed image data to a remote server, the method further includes: and sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
Specifically, since a plurality of image data in the smoothed image data are decomposed from the original image data, the priority of the image data, that is, the degree of optimization and the region of optimization, can be determined according to the optimized weight factor, and the smoothed image data is sorted according to the weight condition, so that the server can better apply the smoothed image data to the work of model processing after receiving the smoothed image data.
By the embodiment, the technical problems that in the prior art, the acquired image optimization transformation method only carries out noise reduction or point location optimization on original image data, and even directly carries out operations such as restoration, gray scale resistance and the like on original pixels, so that the image optimization is incomplete, the optimization effect is poor, and the optimization efficiency is poor are solved.
Example two
Fig. 2 is a block diagram illustrating a structure of a picture transformation apparatus based on mesh grid network decomposition according to an embodiment of the present invention, as shown in fig. 2, the apparatus including:
and the acquisition module 20 is used for acquiring original image information from the high-precision camera equipment.
Specifically, in order to solve the technical problems that the optimization of the image is incomplete, the optimization effect is poor, and the optimization efficiency is poor due to the fact that the collected image optimization transformation method in the prior art only performs noise reduction or point location optimization on the original image data, even directly performs operations such as restoration and gray scale resistance on the original pixels, and overcome related technical defects, the method and the device firstly need to collect and collect original image information needing picture change and optimization from high-precision camera equipment, store the original image, and facilitate subsequent decomposition of the original image information for optimization.
And the decomposition module 22 is configured to decompose the original image information by using a mesh grid decomposition algorithm to obtain grid image data.
Optionally, the decomposition module includes: the acquiring unit is used for acquiring the item factors and the weight factors of the mesh grid network; a calculating unit, configured to calculate the gridding image data according to the polynomial factor and the weighting factor, where a formula for calculating the gridding image data includes:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor.
Specifically, after the original image information is acquired, in order to perform image segmentation and image decomposition by using the algorithm of the mesh grid network, so as to optimize and correct the decomposed images one by one, the method and the device need to acquire the mesh grid network subentry factor and the weighting factor; calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor. That is, in order to obtain an aggregate of the gridded image data, when both n and i specify a parameter, it is necessary to calculate the number of gridded images by incorporating a polynomial factor and a weight factorAnd obtaining gridding image data suitable for optimization according to the range.
And the optimization module 24 is configured to perform an anti-aliasing correction operation and a gray scale optimization operation on the gridding image data to obtain smooth image data.
Specifically, in order to perform an optimization operation on an image processed through a mesh network, the mesh image data may be subjected to an antialiasing correction operation and a grayscale optimization operation, where the antialiasing optimization operation may optimize and smooth pixels or singular points in each image data in the mesh network, and the grayscale optimization operation may perform brightness correction and adjustment on each image data in the mesh image data, so as to obtain smooth image data that can be uploaded and used for model construction.
Optionally, the antialiasing correction operation includes: FXAA correction operation, MSAA correction operation.
In particular, the above FXAA is a high performance approximation of the MSAA effect. It is a one-way pixel shader, running at the post-processing stage of the target game rendering pipeline MLAA, but unlike the latter, which uses directcompute, it is simply a simple post-processing shader, not relying on any GPU computing API. Thus, the fxaa technology does not require a graphics card, and is fully compatible with NVIDIA and AMD, as well as different graphics cards DX9, DX10, and DX 11.
And an uploading module 26, configured to upload the smoothed image data to a remote server.
Specifically, after the smooth image data is acquired, all the smooth image data needs to be uploaded to a remote server, so that subsequent model training or image processing on the smooth image data is facilitated.
Optionally, the apparatus further comprises: and the sequencing module is used for sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
Specifically, since a plurality of image data in the smoothed image data are decomposed from the original image data, the priority of the image data, that is, the degree of optimization and the region of optimization, can be determined according to the optimized weight factor, and the smoothed image data is sorted according to the weight condition, so that the server can better apply the smoothed image data to the work of model processing after receiving the smoothed image data.
By the embodiment, the technical problems that in the prior art, the acquired image optimization transformation method only carries out noise reduction or point location optimization on original image data, and even directly carries out operations such as restoration, gray scale resistance and the like on original pixels, so that the image optimization is incomplete, the optimization effect is poor, and the optimization efficiency is poor are solved.
According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided, where the non-volatile storage medium includes a stored program, and the program controls, when running, a device in which the non-volatile storage medium is located to execute a screen transformation method based on mesh grid network decomposition.
Specifically, the method comprises the following steps: collecting original image information from high-precision camera equipment; decomposing the original image information by a mesh grid decomposition algorithm to obtain grid image data; performing anti-sawtooth correction operation and gray level optimization operation on the gridding image data to obtain smooth image data; and uploading the smooth image data to a remote server. Optionally, decomposing the original image information by using a mesh grid decomposition algorithm to obtain grid image data includes: acquiring a mesh grid network subentry factor and a weight factor; calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor. Optionally, the antialiasing correction operation includes: FXAACorrection operation, MSAA correction operation. Optionally, before uploading the smoothed image data to a remote server, the method further includes: and sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions, and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions when executed perform a picture transformation method based on mesh grid network decomposition.
Specifically, the method includes: collecting original image information from high-precision camera equipment; decomposing the original image information by a mesh grid decomposition algorithm to obtain grid image data; performing anti-aliasing correction operation and gray level optimization operation on the gridding image data to obtain smooth image data; and uploading the smooth image data to a remote server. Optionally, decomposing the original image information by using a mesh grid decomposition algorithm to obtain grid image data includes: acquiring a mesh grid network subentry factor and a weight factor; calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor. Optionally, the antialiasing correction operation includes: FXAA correction operation, MSAA correction operation. Optionally, before uploading the smoothed image data to a remote server, the method further includes: and sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, fig. 3 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present application. As shown in fig. 3, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to implement communication connections between the elements. The memory 33 may comprise a high speed RAM memory, and may also include a non-volatile memory NVM, such as at least one disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the processor 31 may be implemented by, for example, a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 through a wired or wireless connection.
Optionally, the input device 30 may include a variety of input devices, for example, at least one of a user-oriented user interface, a device-oriented device interface, a software programmable interface, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware plug-in interface (e.g., a USB interface, a serial port, etc.) for data transmission between devices; optionally, the user-facing user interface may be, for example, a user-facing control key, a voice input device for receiving voice input, and a touch sensing device (e.g., a touch screen with a touch sensing function, a touch pad, etc.) for receiving user touch input; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, such as an input pin interface or an input interface of a chip; optionally, the transceiver may be a radio frequency transceiver chip with a communication function, a baseband processing chip, a transceiver antenna, and the like. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, a sound, or other output device.
In this embodiment, the processor of the terminal device includes a module for executing the functions of the modules of the data processing apparatus in each device, and specific functions and technical effects may refer to the foregoing embodiments, which are not described herein again.
Fig. 4 is a schematic hardware structure diagram of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of fig. 3 in an implementation process. As shown in fig. 4, the terminal device of the present embodiment includes a processor 41 and a memory 42.
The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above-described embodiment.
The memory 42 is configured to store various types of data to support operations at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, and so forth. The memory 42 may include a Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, the processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The specific components included in the terminal device are set according to actual requirements, which is not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. Processing component 40 may include one or more processors 41 to execute instructions to perform all or a portion of the steps of the above-described method. Further, processing component 40 may include one or more modules that facilitate interaction between processing component 40 and other components. For example, the processing component 40 may include a multimedia module to facilitate interaction between the multimedia component 45 and the processing component 40.
The power supply component 44 provides power to the various components of the terminal device. The power components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the terminal device.
The multimedia component 45 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The audio component 46 is configured to output and/or input audio signals. For example, the audio component 46 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operational mode, such as a voice recognition mode. The received audio signal may further be stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 also includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing component 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: a volume button, a start button, and a lock button.
The sensor assembly 48 includes one or more sensors for providing various aspects of status assessment for the terminal device. For example, the sensor assembly 48 may detect the open/closed status of the terminal device, the relative positioning of the components, the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera or the like.
The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot for inserting a SIM card therein, so that the terminal device can log on to a GPRS network and establish communication with the server via the internet.
From the above, the communication component 43, the audio component 46, the input/output interface 47 and the sensor component 48 referred to in the embodiment of fig. 4 can be implemented as the input device in the embodiment of fig. 3.
In the embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A picture transformation method based on mesh grid network decomposition is characterized by comprising the following steps:
collecting original image information from high-precision camera equipment;
decomposing the original image information by a mesh grid decomposition algorithm to obtain grid image data;
performing anti-aliasing correction operation and gray level optimization operation on the gridding image data to obtain smooth image data;
and uploading the smooth image data to a remote server.
2. The method of claim 1, wherein decomposing the original image information by a mesh grid decomposition algorithm to obtain gridded image data comprises:
acquiring a mesh grid network subentry factor and a weight factor;
calculating to obtain the gridding image data according to the subentry factor and the weighting factor, wherein a formula for calculating the gridding image data comprises the following steps:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor.
3. The method of claim 1, wherein the antialiasing correction operation comprises: FXAA correction operation, MSAA correction operation.
4. The method of claim 1, wherein prior to said uploading said smoothed image data to a remote server, said method further comprises:
and sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
5. A picture transformation apparatus based on mesh network decomposition, comprising:
the acquisition module is used for acquiring original image information from the high-precision camera shooting equipment;
the decomposition module is used for decomposing the original image information through a mesh grid decomposition algorithm to obtain grid image data;
the optimization module is used for performing anti-aliasing correction operation and gray level optimization operation on the gridded image data to obtain smooth image data;
and the uploading module is used for uploading the smooth image data to a remote server.
6. The apparatus of claim 5, wherein the decomposition module comprises:
the acquiring unit is used for acquiring the item factors and the weight factors of the mesh grid network;
a calculating unit, configured to calculate the gridding image data according to the polynomial factor and the weighting factor, where a formula for calculating the gridding image data includes:
where W is the gridded image data set, n is the subentry factor, i is the starting parameter, f p And f H Is a two-dimensional weighting factor.
7. The apparatus of claim 5, wherein the antialiasing correction operation comprises: FXAA correction operation, MSAA correction operation.
8. The apparatus of claim 5, further comprising:
and the sequencing module is used for sequencing the images which are not spliced according to the weight condition of the smooth image data to obtain the sequenced smooth image data.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210990442.XA CN115330626A (en) | 2022-08-18 | 2022-08-18 | Picture transformation method and device based on mesh grid network decomposition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210990442.XA CN115330626A (en) | 2022-08-18 | 2022-08-18 | Picture transformation method and device based on mesh grid network decomposition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115330626A true CN115330626A (en) | 2022-11-11 |
Family
ID=83923524
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210990442.XA Pending CN115330626A (en) | 2022-08-18 | 2022-08-18 | Picture transformation method and device based on mesh grid network decomposition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115330626A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115984126A (en) * | 2022-12-05 | 2023-04-18 | 北京拙河科技有限公司 | Optical image correction method and device based on input instruction |
| CN116363006A (en) * | 2023-03-28 | 2023-06-30 | 北京拙河科技有限公司 | Image calibration method and device based on normal algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109754380A (en) * | 2019-01-02 | 2019-05-14 | 京东方科技集团股份有限公司 | A kind of image processing method and image processing apparatus, display device |
| CN114331810A (en) * | 2020-09-30 | 2022-04-12 | 华为技术有限公司 | Image processing method and related device |
| CN114519663A (en) * | 2021-11-04 | 2022-05-20 | 上海哔哩哔哩科技有限公司 | Image-based deformation method, device, equipment and storage medium |
| CN114565708A (en) * | 2020-11-13 | 2022-05-31 | 华为技术有限公司 | Method, device and equipment for selecting anti-aliasing algorithm and readable storage medium |
| CN114782969A (en) * | 2022-05-11 | 2022-07-22 | 北京鼎泰智源科技有限公司 | Image table data extraction method and device based on generation countermeasure network |
-
2022
- 2022-08-18 CN CN202210990442.XA patent/CN115330626A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109754380A (en) * | 2019-01-02 | 2019-05-14 | 京东方科技集团股份有限公司 | A kind of image processing method and image processing apparatus, display device |
| CN114331810A (en) * | 2020-09-30 | 2022-04-12 | 华为技术有限公司 | Image processing method and related device |
| CN114565708A (en) * | 2020-11-13 | 2022-05-31 | 华为技术有限公司 | Method, device and equipment for selecting anti-aliasing algorithm and readable storage medium |
| CN114519663A (en) * | 2021-11-04 | 2022-05-20 | 上海哔哩哔哩科技有限公司 | Image-based deformation method, device, equipment and storage medium |
| CN114782969A (en) * | 2022-05-11 | 2022-07-22 | 北京鼎泰智源科技有限公司 | Image table data extraction method and device based on generation countermeasure network |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115984126A (en) * | 2022-12-05 | 2023-04-18 | 北京拙河科技有限公司 | Optical image correction method and device based on input instruction |
| CN116363006A (en) * | 2023-03-28 | 2023-06-30 | 北京拙河科技有限公司 | Image calibration method and device based on normal algorithm |
| CN116363006B (en) * | 2023-03-28 | 2024-02-02 | 北京拙河科技有限公司 | Image calibration method and device based on normal algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115330626A (en) | Picture transformation method and device based on mesh grid network decomposition | |
| CN110189246B (en) | Image stylization generation method and device and electronic equipment | |
| CN109086742A (en) | scene recognition method, scene recognition device and mobile terminal | |
| CN115631122A (en) | Image optimization method and device for edge image algorithm | |
| CN115984126A (en) | Optical image correction method and device based on input instruction | |
| CN116614453B (en) | Image transmission bandwidth selection method and device based on cloud interconnection | |
| CN110197459B (en) | Image stylization generation method and device and electronic equipment | |
| CN115293985B (en) | Super-resolution noise reduction method and device for image optimization | |
| CN115484408A (en) | Snow surface reflection coefficient generation method and device based on high-precision camera shooting | |
| CN109089042B (en) | Image processing mode identification method and device, storage medium and mobile terminal | |
| CN110969587A (en) | Image acquisition method and device and electronic equipment | |
| CN111698512B (en) | Video processing method, device, equipment and storage medium | |
| CN114298895A (en) | Image realistic style migration method, device, equipment and storage medium | |
| CN115205313B (en) | Picture optimization method and device based on least square algorithm | |
| CN112258392A (en) | Super-resolution image training method, device, medium and equipment | |
| CN115546053B (en) | Method and device for eliminating diffuse reflection of graphics on snow in complex terrain | |
| CN116664413B (en) | Image volume fog eliminating method and device based on Abbe convergence operator | |
| CN111814840A (en) | Method, system, equipment and medium for evaluating quality of face image | |
| CN115511735B (en) | Snow field gray scale picture optimization method and device | |
| CN115187570B (en) | Singular traversal retrieval method and device based on DNN deep neural network | |
| CN116363006B (en) | Image calibration method and device based on normal algorithm | |
| CN115937338B (en) | Image processing method, device, equipment and medium | |
| CN115460389B (en) | Image white balance area optimization method and device | |
| CN116468883B (en) | High-precision image data volume fog recognition method and device | |
| CN117523136B (en) | Face point position corresponding relation processing method, face reconstruction method, device and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20221111 |