CN110349107B - Image enhancement method, device, electronic equipment and storage medium - Google Patents
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- 238000002372 labelling Methods 0.000 claims description 21
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- 238000013527 convolutional neural network Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000003796 beauty Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
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- G06T5/90—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
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- 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
- G06T2207/10024—Color image
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The embodiment of the disclosure discloses an image enhancement method, an image enhancement device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an original image; generating a thumbnail image corresponding to the original image; inputting the thumbnail image into a pre-trained color lookup table generation model to obtain a color lookup table; and determining an enhanced image corresponding to the original image according to the original image and the color lookup table so as to rapidly acquire the enhanced image of the original image, thereby improving the acquisition efficiency of the enhanced image.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of machine learning, in particular to an image enhancement method, an image enhancement device, electronic equipment and a storage medium.
Background
In the prior art, when a beauty camera is adopted to take photos, the photos taken are usually generated into beauty photos in real time according to a fixed filter set or selected by a user.
The invention generates a three-dimensional lookup table for each source image, and adopts the filter to transform the source images to obtain enhanced images.
The look-up table function is similar to the general filter function. For example, a dark face area is brightened, the entire relatively dark image is brightened, the underexposed picture is corrected, and the larger-color picture is color-corrected.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide a method, an apparatus, an electronic device, and a storage medium for image enhancement, so as to improve efficiency of acquiring an enhanced image.
Other features and advantages of embodiments of the present disclosure will be apparent from the following detailed description, or may be learned by practice of embodiments of the disclosure in part.
In a first aspect, embodiments of the present disclosure provide a method of image enhancement, including:
acquiring an original image;
generating a thumbnail image corresponding to the original image;
inputting the thumbnail image into a pre-trained color lookup table generation model to obtain a color lookup table;
and determining an enhanced image corresponding to the original image according to the original image and the color lookup table.
In a second aspect, embodiments of the present disclosure further provide an apparatus for image enhancement, including:
an original image acquisition unit configured to acquire an original image;
a thumbnail image generation unit for generating a thumbnail image corresponding to the original image;
a color lookup table obtaining unit, configured to input the thumbnail image into a pre-trained color lookup table generating model, to obtain a color lookup table;
and the enhanced image determining unit is used for determining an enhanced image corresponding to the original image according to the original image and the color lookup table.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the instructions of the method of any of the first aspects.
In a fourth aspect, the presently disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of the first aspects.
According to the method, the device and the system, the thumbnail images are generated by acquiring the original images, the thumbnail images are input into the pre-trained color lookup table generation model, the color lookup table is obtained, the enhanced images corresponding to the original images are determined according to the original images and the color lookup table, the enhanced images are quickly acquired according to the thumbnail images of the original images, and the efficiency of acquiring the enhanced images can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following description will briefly explain the drawings required to be used in the description of the embodiments of the present disclosure, and it is apparent that the drawings in the following description are only some of the embodiments of the present disclosure, and other drawings may be obtained according to the contents of the embodiments of the present disclosure and these drawings without inventive effort for those skilled in the art.
FIG. 1 is a flow chart of a method of image enhancement provided by an embodiment of the present disclosure;
FIG. 2 is a flow chart of a training method for generating a model from a color look-up table according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an apparatus for image enhancement according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a training device for generating a model from a color look-up table according to an embodiment of the present disclosure;
fig. 5 shows a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
In order to make the technical problems solved, the technical solutions adopted and the technical effects achieved by the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments, but not all embodiments of the present disclosure. All other embodiments, which are derived by a person skilled in the art from the embodiments of the present disclosure without creative efforts, fall within the protection scope of the embodiments of the present disclosure.
It should be noted that the terms "system" and "network" in the embodiments of the present disclosure are often used interchangeably herein. References to "and/or" in embodiments of the present disclosure are intended to "include any and all combinations of one or more of the associated listed items. The terms first, second and the like in the description and in the claims and drawings are used for distinguishing between different objects and not for limiting a particular order.
It should be further noted that, in the embodiments of the present disclosure, the following embodiments may be implemented separately, or may be implemented in combination with each other, which is not specifically limited by the embodiments of the present disclosure.
The technical solutions of the embodiments of the present disclosure are further described below with reference to the accompanying drawings and through specific implementations.
Fig. 1 is a flow chart illustrating a method for image enhancement provided in an embodiment of the present disclosure, where the embodiment may be applicable to a case of obtaining an enhanced image of an original image, and the method may be performed by an image enhancement apparatus configured in an electronic device, as shown in fig. 1, where the method for image enhancement includes:
in step S110, an original image is acquired.
The original image in this embodiment may be any type of image, for example, may be an original image including a face, so as to perform a beautifying and image repairing process or filter shooting by using the technical scheme in this embodiment.
It should be noted that, the original image may be a pre-shot image, or may be a photo acquired by a camera in real time, and the photo is cached in a buffer area to be used as the original image. For the former, the technical solution of the present embodiment may be used to perform post-processing on a picture to generate an enhancement image thereof, and for the latter, the technical solution of the present embodiment may be used to perform filter shooting in real time to shoot an enhancement image to be processed, for example, to lighten a dark place of a face in an image, to lighten a relatively dark image as a whole, to correct an underexposed picture, and/or to correct a picture with a larger color.
In step S120, a thumbnail image corresponding to the original image is generated.
In step S130, the thumbnail image is input to a pre-trained color look-up table generation model, resulting in a color look-up table.
When the color lookup table is generated by the color lookup table generation model, the result is hardly different regardless of whether the original image or the thumbnail image is input, but the thumbnail image is smaller in size than the original image and therefore the calculation amount is much smaller, so that the color lookup table is obtained by inputting the thumbnail image of the original image into the color lookup table generation model in this step, and the efficiency is significantly high.
The color lookup table generating model according to the present embodiment is required to obtain a color lookup table corresponding to an original image after the original image is input, and the training method and characteristics of the specific model are not limited in this embodiment, and an independent exemplary embodiment is provided after this embodiment. The color lookup table may be a one-dimensional color lookup table or a 3D color lookup table, which is not limited in this embodiment.
In step S140, an enhanced image corresponding to the original image is determined according to the original image and the color lookup table.
For example, each pixel value in the original image may be input into the color lookup table, and the output result of the color lookup table is used as the pixel value of the corresponding pixel of the enhanced image.
According to the technical scheme, the thumbnail images are generated by acquiring the original images, the thumbnail images are input into a pre-trained color lookup table generation model, a color lookup table is obtained, the enhanced images corresponding to the original images are determined according to the original images and the color lookup table, the enhanced images are quickly acquired according to the thumbnail images of the original images, and the efficiency of acquiring the enhanced images can be improved.
Fig. 2 is a flowchart of a training method for generating a model by using a color lookup table according to an embodiment of the present disclosure, as shown in fig. 2, where the training method for generating a model by using a color lookup table according to the present embodiment includes:
in step S210, a training sample set is acquired.
Wherein the training sample comprises a sample image and a corresponding enhanced image.
In step S220, a labeling color lookup table for converting the sample image into the enhanced image is generated according to the sample image and the corresponding enhanced image included in the sample, and the labeling color lookup table is used as a label of the sample.
In step S230, it is determined that the initialized color look-up table generation model.
Wherein the initialized color lookup table generation model comprises a target layer for outputting a color lookup table corresponding to a target image.
The initialized color look-up table generation model may be various types of untrained or untrained artificial neural networks, such as machine learning models based on convolutional neural network techniques.
In step S240, a sample image in a training sample in the training sample set is used as an input of an initialized color lookup table generating model, a labeled color lookup table corresponding to the input sample image is used as an expected output of the initialized color lookup table generating model, and the color lookup table generating model is obtained through training by using a machine learning method.
The technical scheme of the embodiment discloses a training method for a color lookup table generation model, which comprises the steps of acquiring a training sample set, determining an initialized color lookup table generation model, using a machine learning method, taking a sample image in a training sample in the training sample set as input of the initialized color lookup table generation model, taking a labeling color lookup table corresponding to the input sample image as expected output of the initialized color lookup table generation model, and training to obtain the color lookup table generation model.
Fig. 3 shows a schematic structural diagram of an image enhancement apparatus provided in an embodiment of the present disclosure, and as shown in fig. 3, the image enhancement apparatus according to the present embodiment includes an original image acquisition unit 310, a thumbnail image generation unit 320, a color lookup table acquisition unit 330, and an enhanced image determination unit 340.
Wherein the original image acquisition unit 310 is configured to acquire an original image.
The thumbnail image generation unit 320 is configured to generate a thumbnail image corresponding to the original image.
The color lookup table obtaining unit 330 is configured to input the thumbnail image to a pre-trained color lookup table generating model, resulting in a color lookup table.
The enhanced image determining unit 340 is configured to determine an enhanced image corresponding to the original image according to the original image and the color lookup table.
Further, the color lookup table is a 3D color lookup table.
Further, the enhanced image determining unit 340 is configured to input each pixel value in the original image into the color lookup table, and take the output result of the color lookup table as the pixel value of the corresponding pixel of the enhanced image.
Further, the original image obtaining unit 310 is configured to obtain a photograph collected by the camera, and buffer the photograph into a buffer area as the original image.
Further, the original image is an original image including a human face.
Further, the color lookup table generating model in the color lookup table obtaining unit 330 is obtained through training by each module of the training device of the color lookup table generating model.
The image enhancement device provided by the embodiment of the invention can execute the image enhancement method provided by the embodiment of the method of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 4 is a schematic structural diagram of a training device for generating a model by using a color lookup table according to an embodiment of the present disclosure, as shown in fig. 4, where the training device for generating a model by using a color lookup table according to the present embodiment includes a sample acquiring module 410, a sample labeling module 420, a model determining module 430, and a model training module 440.
Wherein the sample acquisition module 410 is configured to acquire a training sample set.
Wherein the training sample comprises a sample image and a corresponding enhanced image.
The sample labeling module 420 is configured to generate a labeling color lookup table for converting the sample image into the enhanced image according to the sample image and the corresponding enhanced image included in the sample, and take the labeling color lookup table as a label of the sample.
The model determination module 430 is configured to determine an initialized color look-up table generation model, wherein the initialized color look-up table generation model includes a target layer for outputting a color look-up table corresponding to a target image.
The model training module 440 is configured to use a machine learning device to use a sample image in a training sample in the training sample set as an input of an initialized color lookup table generating model, and use a labeled color lookup table corresponding to the input sample image as an expected output of the initialized color lookup table generating model, and train to obtain the color lookup table generating model.
Further, the color lookup table generation model is a machine learning model based on convolutional neural network technology.
The training device for the color lookup table generation model provided by the embodiment can execute the training method for the color lookup table generation model provided by the embodiment of the method disclosed by the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Referring now to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.
It should be noted that, the computer readable medium described above in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the disclosed embodiments, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the disclosed embodiments, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
acquiring an original image;
generating a thumbnail image corresponding to the original image;
inputting the thumbnail image into a pre-trained color lookup table generation model to obtain a color lookup table;
and determining an enhanced image corresponding to the original image according to the original image and the color lookup table.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".
According to one or more embodiments of the present disclosure, in the image enhancement method, the color lookup table is a 3D color lookup table.
According to one or more embodiments of the present disclosure, in the image enhancement method, determining an enhanced image corresponding to the original image according to the original image and the color lookup table includes: and respectively inputting each pixel value in the original image into the color lookup table, and taking the output result of the color lookup table as the pixel value of the corresponding pixel of the enhanced image.
In accordance with one or more embodiments of the present disclosure, in the image enhancement method, acquiring the original image includes: and acquiring a photo acquired by the camera, and caching the photo into a buffer area to serve as the original image.
According to one or more embodiments of the present disclosure, in the image enhancement method, the original image is an original image including a face.
According to one or more embodiments of the present disclosure, in the image enhancement method, the color look-up table generation model is trained by:
acquiring a training sample set, wherein the training sample comprises a sample image and a corresponding enhanced image;
generating a labeling color lookup table for converting the sample image into the enhanced image according to the sample image and the corresponding enhanced image included in the sample, and taking the labeling color lookup table as a labeling of the sample;
determining an initialized color lookup table generation model, wherein the initialized color lookup table generation model comprises a target layer of a color lookup table corresponding to an output target image;
and using a machine learning method, taking a sample image in a training sample in the training sample set as input of an initialized color lookup table generation model, taking a labeling color lookup table corresponding to the input sample image as expected output of the initialized color lookup table generation model, and training to obtain the color lookup table generation model.
In accordance with one or more embodiments of the present disclosure, in the method of image enhancement, the color look-up table generation model is a machine learning model based on convolutional neural network technology.
In accordance with one or more embodiments of the present disclosure, in the image enhancement apparatus, the color lookup table is a 3D color lookup table.
According to one or more embodiments of the present disclosure, in the image enhancement apparatus, the enhanced image determining unit is configured to: and respectively inputting each pixel value in the original image into the color lookup table, and taking the output result of the color lookup table as the pixel value of the corresponding pixel of the enhanced image.
According to one or more embodiments of the present disclosure, in the image enhancement apparatus, the original image acquisition unit is configured to: and acquiring a photo acquired by the camera, and caching the photo into a buffer area to serve as the original image.
According to one or more embodiments of the present disclosure, in the image enhancement apparatus, the original image is an original image including a face.
According to one or more embodiments of the present disclosure, in the image enhancement apparatus, the color look-up table generating model is trained by:
the sample acquisition module is used for acquiring a training sample set, wherein the training sample comprises a sample image and a corresponding enhanced image;
the sample labeling module is used for generating a labeling color lookup table for converting the sample image into the enhanced image according to the sample image and the corresponding enhanced image included in the sample, and taking the labeling color lookup table as a label of the sample;
the model determining module is used for determining an initialized color lookup table generation model, wherein the initialized color lookup table generation model comprises a target layer of a color lookup table corresponding to an output target image;
the model training module is used for using a machine learning device, taking sample images in training samples in the training sample set as input of an initialized color lookup table generation model, taking a marked color lookup table corresponding to the input sample images as expected output of the initialized color lookup table generation model, and training to obtain the color lookup table generation model.
In accordance with one or more embodiments of the present disclosure, in the image enhancement apparatus, the color look-up table generation model is a machine learning model based on convolutional neural network technology.
The foregoing description is only of the preferred embodiments of the disclosed embodiments and is presented for purposes of illustration of the principles of the technology being utilized. It will be appreciated by those skilled in the art that the scope of the disclosure in the embodiments of the disclosure is not limited to the specific combination of the above technical features, but also encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the disclosure. Such as the technical solution formed by mutually replacing the above-mentioned features and the technical features with similar functions (but not limited to) disclosed in the embodiments of the present disclosure.
Claims (8)
1. A method of image enhancement, comprising:
acquiring an original image;
generating a thumbnail image corresponding to the original image;
inputting the thumbnail image into a pre-trained color lookup table generation model to obtain a color lookup table, wherein the color lookup table is a 3D color lookup table;
determining an enhanced image corresponding to the original image according to the original image and the color lookup table;
the color lookup table generation model is obtained through training by the following steps:
acquiring a training sample set, wherein the training sample comprises a sample image and a corresponding enhanced image;
generating a labeling color lookup table for converting the sample image into the enhanced image according to the sample image and the corresponding enhanced image included in the sample, and taking the labeling color lookup table as a labeling of the sample;
determining an initialized color lookup table generation model, wherein the initialized color lookup table generation model comprises a target layer of a color lookup table corresponding to an output target image;
and using a machine learning method, taking a sample image in a training sample in the training sample set as input of an initialized color lookup table generation model, taking a labeling color lookup table corresponding to the input sample image as expected output of the initialized color lookup table generation model, and training to obtain the color lookup table generation model.
2. The method of claim 1, wherein determining an enhanced image corresponding to the original image from the original image and the color look-up table comprises: and respectively inputting each pixel value in the original image into the color lookup table, and taking the output result of the color lookup table as the pixel value of the corresponding pixel of the enhanced image.
3. The method of claim 1, wherein acquiring the original image comprises:
and acquiring a photo acquired by the camera, and caching the photo into a buffer area to serve as the original image.
4. A method according to claim 3, wherein the original image is an original image comprising a face.
5. The method of claim 1, wherein the color look-up table generation model is a machine learning model based on convolutional neural network technology.
6. An apparatus for image enhancement, comprising:
an original image acquisition unit configured to acquire an original image;
a thumbnail image generation unit for generating a thumbnail image corresponding to the original image;
a color lookup table obtaining unit, configured to input the thumbnail image to a pre-trained color lookup table generating model, to obtain a color lookup table, where the color lookup table is a 3D color lookup table;
an enhanced image determining unit, configured to determine an enhanced image corresponding to the original image according to the original image and the color lookup table;
the training device for generating the model by using the color lookup table comprises:
the sample acquisition module is used for acquiring a training sample set, wherein the training sample comprises a sample image and a corresponding enhanced image;
the sample labeling module is used for generating a labeling color lookup table for converting the sample image into the enhanced image according to the sample image and the corresponding enhanced image included in the sample, and taking the labeling color lookup table as a label of the sample;
the model determining module is used for determining an initialized color lookup table generation model, wherein the initialized color lookup table generation model comprises a target layer of a color lookup table corresponding to an output target image;
the model training module is used for using a machine learning device, taking sample images in training samples in the training sample set as input of an initialized color lookup table generation model, taking a marked color lookup table corresponding to the input sample images as expected output of the initialized color lookup table generation model, and training to obtain the color lookup table generation model.
7. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
the instructions that when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-5.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-5.
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