CN115511735B - Snow field gray scale picture optimization method and device - Google Patents
Snow field gray scale picture optimization method and device Download PDFInfo
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Abstract
The invention discloses a snow field gray scale picture optimization method and device. Wherein the method comprises the following steps: acquiring original image data of a snow field; carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data; decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced; and performing splicing operation on the image data to be spliced to obtain a target snow field picture. The invention solves the technical problems that in the prior art, the snow image optimization processing process only adopts a direct optimization and pixel parameter processing method for original image data, and further processing can not be carried out on the snow original image data collected by the snow camera equipment, so that the optimization strength is increased, the optimization inaccuracy is reduced, the distortion rate is too high, and the optimization efficiency is lower.
Description
Technical Field
The invention relates to the field of image optimization processing, in particular to a snow field gray scale image optimization method and device.
Background
Along with the continuous development of intelligent science and technology, intelligent equipment is increasingly used in life, work and study of people, and the quality of life of people is improved and the learning and working efficiency of people is increased by using intelligent science and technology means.
At present, a high-definition camera is generally adopted to collect original image data of a field monitoring area of a snowfield, and the original image data is subjected to pixel statistics and pixel optimization processing, so that image data with higher sharpening degree and cleaner image data are generated, but in the prior art, the snow image optimization processing process only adopts a direct optimization and pixel parameter processing method for the original image data, and further processing cannot be carried out on the snow original image data collected by a snow imaging device, so that the optimization force is increased, the optimization inaccuracy is reduced, the distortion rate is too high, and the optimization efficiency is lower.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a snow field gray scale picture optimizing method and a snow field gray scale picture optimizing device, which at least solve the technical problems that in the prior art, the snow field gray scale picture optimizing process is at least realized, the original image data cannot be further processed by adopting a direct optimizing and pixel parameter processing method, and the snow field original image data collected by snow field image pickup equipment can not be further processed, so that the optimizing strength is increased, the inaccurate optimizing is reduced, the distortion rate is too high, and the optimizing efficiency is lower.
According to an aspect of the embodiment of the present invention, there is provided a snow field gray scale picture optimizing method, including: acquiring original image data of a snow field; carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data; decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced; and performing splicing operation on the image data to be spliced to obtain a target snow field picture.
Optionally, the performing gray scale preprocessing on the original image data of the snowfield, and generating gray scale image data includes: acquiring environmental parameters of a snow field; generating a gray preset parameter according to the snowfield environment parameter, wherein the gray preset parameter represents the degree and the range of gray processing, and the snowfield environment parameter comprises: snow field illuminance, snow field reflection degree; and carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data.
Optionally, the decomposing the gray image data into an image set to be optimized by using a carteristic decomposition algorithm, and performing an optimization operation on image units in the image set to be optimized, so as to obtain image data to be spliced includes: by the formula
P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = Σp (xi) log (2, P (xi)) (i=1, 2,..n) dividing and isolating pixel parameters in the gray scale image data to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, I is a natural integer greater than 0; and optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced.
Optionally, before the splicing operation is performed on the image data to be spliced to obtain the target snow field picture, the method further includes: pre-stitching the images to be stitched according to the association factors of the image units in the image set to be optimized, wherein the pre-stitching comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
According to another aspect of the embodiment of the present invention, there is also provided a snow field gray scale screen optimizing apparatus, including: the acquisition module is used for acquiring original image data of the snow field; the preprocessing module is used for carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data; the decomposition module is used for decomposing the gray image data into an image set to be optimized by using a Kate decomposition algorithm, and carrying out optimization operation on image units in the image set to be optimized to obtain image data to be spliced; and the splicing module is used for carrying out splicing operation on the image data to be spliced to obtain a target snow field picture.
Optionally, the preprocessing module includes: the acquisition unit is used for acquiring the environmental parameters of the snow field; the generating unit is used for generating gray preset parameters according to the snowfield environment parameters, wherein the gray preset parameters represent the degree and the range of gray processing, and the snowfield environment parameters comprise: snow field illuminance, snow field reflection degree; and the processing unit is used for carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data.
Optionally, the decomposition module includes: a decomposition unit for passing through formula
P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = Σp (xi) log (2, P (xi)) (i=1, 2,..n) dividing and isolating pixel parameters in the gray scale image data to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, I is a natural integer greater than 0; and the optimizing unit is used for optimizing the image set to be optimized by utilizing an image gray scale sharpening model to obtain the image data to be spliced.
Optionally, the apparatus further includes: the splicing unit is used for pre-splicing the images to be spliced according to the association factors of the image units in the image set to be optimized, wherein the pre-splicing comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
According to another aspect of the embodiment of the present invention, there is further provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, the device in which the non-volatile storage medium is controlled to execute a snow field gray scale screen optimization method.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a snow field grayscale image optimization method when executed.
In the embodiment of the invention, the original image data of the snow field is acquired; carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data; decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced; the method for splicing the image data to be spliced to obtain the target snowfield picture solves the technical problems that in the prior art, the original image data cannot be further processed by adopting a direct optimization and pixel parameter processing method for the original image data, so that the optimization strength is increased, the optimization inaccuracy is reduced, the distortion rate is too high, and the optimization efficiency is low.
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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 embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a snowfield gray screen optimization method according to an embodiment of the invention;
FIG. 2 is a block diagram of a snow field gray scale screen optimizing apparatus according to an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing the method according to the invention according to an embodiment of the invention;
fig. 4 is a memory unit for holding or carrying program code for implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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 snow field grayscale screen optimization method, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that illustrated herein.
Example 1
Fig. 1 is a flowchart of a snow field gray scale screen optimizing method according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S102, acquiring original image data of a snow field.
Specifically, the embodiment of the invention does not solve the technical problems that in the prior art, the snow image optimization processing process only adopts a direct optimization and pixel parameter processing method for original image data, and further processing cannot be performed on the snow original image data acquired by the snow image pickup device, so that the optimization strength is increased, the inaccuracy of optimization is reduced, the distortion rate is too high, and the optimization efficiency is low.
Step S104, gray scale preprocessing is carried out on the original image data of the snow field, and gray scale image data is generated.
Specifically, after the original image data is obtained, the original image data is required to be subjected to gray preprocessing, namely, the original image data is grayed to a certain extent, so that an dominant optimization method is adopted when the gray image is optimized later, and the optimization efficiency and success rate are increased.
Optionally, the performing gray scale preprocessing on the original image data of the snowfield, and generating gray scale image data includes: acquiring environmental parameters of a snow field; generating a gray preset parameter according to the snowfield environment parameter, wherein the gray preset parameter represents the degree and the range of gray processing, and the snowfield environment parameter comprises: snow field illuminance, snow field reflection degree; and carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data.
In particular, in order to generate the gray image data described in the embodiments of the present invention, gray preset parameters for characterizing the processing degree and processing method of gray preprocessing may be generated from the raw image data collected by the snow field and the environmental parameters,
and S106, decomposing the gray image data into an image set to be optimized by using a Kate decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced.
Optionally, the decomposing the gray image data into an image set to be optimized by using a carteristic decomposition algorithm, and performing an optimization operation on image units in the image set to be optimized, so as to obtain image data to be spliced includes: by the formula
P(x)＝K[I(xi)]＝K[log(2,1/P(xi))]＝-∑P(xi)log(2,P(xi))(i＝1,2,..n)
Dividing and isolating pixel parameters in the gray image data to form an image set to be optimized comprising a plurality of independent image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, and i is a natural integer greater than 0; and optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced.
Specifically, after the gray image data is generated by the original image data in the embodiment of the invention, in order to increase the optimization precision and efficiency, the whole gray image data (pixel data set) can be decomposed and classified by a Kate decomposition algorithm to obtain a plurality of individual image units, the image units can be respectively optimized to achieve the effects of large size and small classification, so that the optimization effect of the whole image data is increased, the decomposed image units are spliced in the subsequent operation to restore the state of the original image data, and the image data is the optimized image data after restoration. For example, the decomposing the gray image data into an image set to be optimized by using a carter decomposition algorithm, and performing an optimization operation on image units in the image set to be optimized, to obtain image data to be spliced includes: dividing and isolating pixel parameters in the gray image data by a formula P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = - Σp (xi) log (2, P (xi)) (i=1, 2,..n) to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, and I is a natural integer greater than 0; and optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced.
And S108, performing splicing operation on the image data to be spliced to obtain a target snow field picture.
Specifically, in order to obtain the final optimized target snow field picture, the embodiment of the invention needs to splice the image data which is not spliced and output the spliced image as the final optimized snow field image data, thereby facilitating the subsequent analysis and processing of the snow field image data and increasing the success rate and efficiency of snow field dangerous signal identification and snow field motion capture.
Optionally, before the splicing operation is performed on the image data to be spliced to obtain the target snow field picture, the method further includes: pre-stitching the images to be stitched according to the association factors of the image units in the image set to be optimized, wherein the pre-stitching comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
Through the embodiment, the technical problems that in the prior art, the snow image optimization processing process is solved, the snow original image data collected by the snow image pickup device cannot be further processed by adopting the direct optimization and pixel parameter processing method of the original image data, so that the optimization force is increased, the optimization inaccuracy is reduced, the distortion rate is too high, and the optimization efficiency is low are solved.
Example two
Fig. 2 is a block diagram of a snow field gray-scale screen optimizing apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
the acquiring module 20 is configured to acquire original image data of the snow field.
Specifically, the embodiment of the invention does not solve the technical problems that in the prior art, the snow image optimization processing process only adopts a direct optimization and pixel parameter processing method for original image data, and further processing cannot be performed on the snow original image data acquired by the snow image pickup device, so that the optimization strength is increased, the inaccuracy of optimization is reduced, the distortion rate is too high, and the optimization efficiency is low.
The preprocessing module 22 is configured to perform gray-scale preprocessing on the original image data of the snowfield, and generate gray-scale image data.
Specifically, after the original image data is obtained, the original image data is required to be subjected to gray preprocessing, namely, the original image data is grayed to a certain extent, so that an dominant optimization method is adopted when the gray image is optimized later, and the optimization efficiency and success rate are increased.
Optionally, the preprocessing module includes: the acquisition unit is used for acquiring the environmental parameters of the snow field; the generating unit is used for generating gray preset parameters according to the snowfield environment parameters, wherein the gray preset parameters represent the degree and the range of gray processing, and the snowfield environment parameters comprise: snow field illuminance, snow field reflection degree; and the processing unit is used for carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data.
In particular, in order to generate the gray image data described in the embodiments of the present invention, gray preset parameters for characterizing the processing degree and processing method of gray preprocessing may be generated from the raw image data collected by the snow field and the environmental parameters,
the decomposition module 24 is configured to decompose the gray image data into an image set to be optimized by using a carter decomposition algorithm, and perform an optimization operation on image units in the image set to be optimized to obtain image data to be spliced.
Optionally, the decomposition module includes: a decomposition unit for passing through formula
P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = Σp (xi) log (2, P (xi)) (i=1, 2,..n) dividing and isolating pixel parameters in the gray scale image data to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, I is a natural integer greater than 0; and the optimizing unit is used for optimizing the image set to be optimized by utilizing an image gray scale sharpening model to obtain the image data to be spliced.
Specifically, after the gray image data is generated by the original image data in the embodiment of the invention, in order to increase the optimization precision and efficiency, the whole gray image data (pixel data set) can be decomposed and classified by a Kate decomposition algorithm to obtain a plurality of individual image units, the image units can be respectively optimized to achieve the effects of large size and small classification, so that the optimization effect of the whole image data is increased, the decomposed image units are spliced in the subsequent operation to restore the state of the original image data, and the image data is the optimized image data after restoration. For example, the decomposing the gray image data into an image set to be optimized by using a carter decomposition algorithm, and performing an optimization operation on image units in the image set to be optimized, to obtain image data to be spliced includes: dividing and isolating pixel parameters in the gray image data by a formula P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = - Σp (xi) log (2, P (xi)) (i=1, 2,..n) to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, and I is a natural integer greater than 0; and optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced.
And the splicing module 26 is used for carrying out splicing operation on the image data to be spliced to obtain a target snow field picture.
Specifically, in order to obtain the final optimized target snow field picture, the embodiment of the invention needs to splice the image data which is not spliced and output the spliced image as the final optimized snow field image data, thereby facilitating the subsequent analysis and processing of the snow field image data and increasing the success rate and efficiency of snow field dangerous signal identification and snow field motion capture.
Optionally, the apparatus further includes: the splicing unit is used for pre-splicing the images to be spliced according to the association factors of the image units in the image set to be optimized, wherein the pre-splicing comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
Through the embodiment, the technical problems that in the prior art, the snow image optimization processing process is solved, the snow original image data collected by the snow image pickup device cannot be further processed by adopting the direct optimization and pixel parameter processing method of the original image data, so that the optimization force is increased, the optimization inaccuracy is reduced, the distortion rate is too high, and the optimization efficiency is low are solved.
According to another aspect of the embodiment of the present invention, there is further provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, the device in which the non-volatile storage medium is controlled to execute a snow field gray scale screen optimization method.
Specifically, the method comprises the following steps: acquiring original image data of a snow field; carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data; decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced; and performing splicing operation on the image data to be spliced to obtain a target snow field picture. Optionally, the performing gray scale preprocessing on the original image data of the snowfield, and generating gray scale image data includes: acquiring environmental parameters of a snow field; generating a gray preset parameter according to the snowfield environment parameter, wherein the gray preset parameter represents the degree and the range of gray processing, and the snowfield environment parameter comprises: snow field illuminance, snow field reflection degree; and carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data. Optionally, the decomposing the gray image data into an image set to be optimized by using a carteristic decomposition algorithm, and performing an optimization operation on image units in the image set to be optimized, so as to obtain image data to be spliced includes: dividing and isolating pixel parameters in the gray image data by a formula P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = - Σp (xi) log (2, P (xi)) (i=1, 2,..n) to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, and I is a natural integer greater than 0; and optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced. Optionally, before the splicing operation is performed on the image data to be spliced to obtain the target snow field picture, the method further includes: pre-stitching the images to be stitched according to the association factors of the image units in the image set to be optimized, wherein the pre-stitching comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a snow field grayscale image optimization method when executed.
Specifically, the method comprises the following steps: acquiring original image data of a snow field; carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data; decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced; and performing splicing operation on the image data to be spliced to obtain a target snow field picture. Optionally, the performing gray scale preprocessing on the original image data of the snowfield, and generating gray scale image data includes: acquiring environmental parameters of a snow field; generating a gray preset parameter according to the snowfield environment parameter, wherein the gray preset parameter represents the degree and the range of gray processing, and the snowfield environment parameter comprises: snow field illuminance, snow field reflection degree; and carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data. Optionally, the decomposing the gray image data into an image set to be optimized by using a carteristic decomposition algorithm, and performing an optimization operation on image units in the image set to be optimized, so as to obtain image data to be spliced includes: dividing and isolating pixel parameters in the gray image data by a formula P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = - Σp (xi) log (2, P (xi)) (i=1, 2,..n) to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, and I is a natural integer greater than 0; and optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced. Optionally, before the splicing operation is performed on the image data to be spliced to obtain the target snow field picture, the method further includes: pre-stitching the images to be stitched according to the association factors of the image units in the image set to be optimized, wherein the pre-stitching comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, fig. 3 is a schematic 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 enable communication connections between the elements. The memory 33 may comprise a high-speed RAM memory or may further comprise a non-volatile memory NVM, such as at least one magnetic 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 as, for example, a central processing unit (Central Processing Unit, abbreviated as 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 wired or wireless connections.
Alternatively, the input device 30 may include a variety of input devices, for example, may include at least one of a user-oriented user interface, a device-oriented device interface, a programmable interface of software, 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 insertion interface (such as a USB interface, a serial port, etc.) for data transmission between devices; alternatively, the user-oriented user interface may be, for example, a user-oriented control key, a voice input device for receiving voice input, and a touch-sensitive device (e.g., a touch screen, a touch pad, etc. having touch-sensitive functionality) for receiving user touch input by a user; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, for example, an input pin interface or an input interface of a chip, etc.; optionally, the transceiver may be a radio frequency transceiver chip, a baseband processing chip, a transceiver antenna, etc. with a communication function. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, audio, or the like.
In this embodiment, the processor of the terminal device may include functions for executing each module of the data processing apparatus in each device, and specific functions and technical effects may be referred to the above embodiments and are not described herein again.
Fig. 4 is a schematic hardware structure of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of the implementation of fig. 3. 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 methods of the above-described embodiments.
The memory 42 is configured to store various types of data to support operation at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, video, etc. The memory 42 may include a random access memory (random access memory, simply referred to as RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power supply component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The components and the like specifically included in the terminal device are set according to actual requirements, which are not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 40 may include one or more modules that facilitate interactions between the processing component 40 and other components. For example, processing component 40 may include a multimedia module to facilitate interaction between multimedia component 45 and processing component 40.
The power supply assembly 44 provides power to the various components of the terminal device. Power supply components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for terminal devices.
The multimedia component 45 comprises a display screen between the terminal device and the user providing an output interface. 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 input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also 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 speech recognition mode. The received audio signals may be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 further includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing assembly 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: volume button, start button and lock button.
The sensor assembly 48 includes one or more sensors for providing status assessment of various aspects for the terminal device. For example, the sensor assembly 48 may detect the open/closed state of the terminal device, the relative positioning of the assembly, 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 in the absence of 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, where the SIM card slot is used to insert a SIM card, so that the terminal device may log into a GPRS network, and establish communication with a server through the internet.
From the above, it will be appreciated that the communication component 43, the audio component 46, and the input/output interface 47, the sensor component 48 referred to in the embodiment of fig. 4 may be implemented as an input device in the embodiment of fig. 3.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (4)
1. The snow field gray scale picture optimizing method is characterized by comprising the following steps:
acquiring original image data of a snow field;
carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data;
decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized to obtain image data to be spliced;
performing splicing operation on the image data to be spliced to obtain a target snow field picture;
the step of carrying out gray scale preprocessing on the original image data of the snow field to generate gray scale image data comprises the following steps:
acquiring environmental parameters of a snow field;
generating a gray preset parameter according to the snowfield environment parameter, wherein the gray preset parameter represents the degree and the range of gray processing, and the snowfield environment parameter comprises: snow field illuminance, snow field reflection degree;
carrying out gray scale processing on the original image data of the snow field by utilizing the gray scale preset parameters to obtain gray scale image data;
decomposing the gray image data into an image set to be optimized by using a Katt decomposition algorithm, and performing optimization operation on image units in the image set to be optimized, wherein obtaining the image data to be spliced comprises the following steps:
by the formula
P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = Σp (xi) log (2, P (xi)) (i=1, 2,..n) dividing and isolating pixel parameters in the gray scale image data to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, I is a natural integer greater than 0;
optimizing the image set to be optimized by using an image gray scale sharpening model to obtain the image data to be spliced;
before the image data to be spliced is subjected to splicing operation to obtain a target snow field picture, the method further comprises the following steps:
pre-stitching the images to be stitched according to the association factors of the image units in the image set to be optimized, wherein the pre-stitching comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
2. A snow field gray scale picture optimizing apparatus, comprising:
the acquisition module is used for acquiring original image data of the snow field;
the preprocessing module is used for carrying out gray level preprocessing on the original image data of the snow field to generate gray level image data;
the decomposition module is used for decomposing the gray image data into an image set to be optimized by using a Kate decomposition algorithm, and carrying out optimization operation on image units in the image set to be optimized to obtain image data to be spliced;
the splicing module is used for carrying out splicing operation on the image data to be spliced to obtain a target snow field picture;
the preprocessing module comprises:
the acquisition unit is used for acquiring the environmental parameters of the snow field;
the generating unit is used for generating gray preset parameters according to the snowfield environment parameters, wherein the gray preset parameters represent the degree and the range of gray processing, and the snowfield environment parameters comprise: snow field illuminance, snow field reflection degree;
the processing unit is used for carrying out gray processing on the original image data of the snow field by utilizing the gray preset parameters to obtain the gray image data;
the decomposition module comprises:
a decomposition unit for passing through formula
P (x) =k [ I (xi) ]=k [ log (2, 1/P (xi)) ] = Σp (xi) log (2, P (xi)) (i=1, 2,..n) dividing and isolating pixel parameters in the gray scale image data to form an image set to be optimized comprising a plurality of individual image units, wherein P is the decomposed image set to be optimized, x is the number of decomposition layers, I is a natural integer greater than 0;
the optimizing unit is used for optimizing the image set to be optimized by utilizing an image gray scale sharpening model to obtain the image data to be spliced;
the apparatus further comprises:
the splicing unit is used for pre-splicing the images to be spliced according to the association factors of the image units in the image set to be optimized, wherein the pre-splicing comprises: and merging the similar edge pictures of the image unit by using the splicing size of N/2.
3. A non-volatile storage medium comprising a stored program, wherein the program when run controls a device in which the non-volatile storage medium resides to perform the method of claim 1.
4. 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 claim 1.
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