CN109523485B - Image color correction method, device, storage medium and mobile terminal - Google Patents
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Abstract
The embodiment of the application discloses an image color correction method, an image color correction device, a storage medium and a mobile terminal. The method comprises the following steps: acquiring an original image to be processed; inputting the original image into a pre-trained image color correction model; and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image. By adopting the technical scheme, the color correction can be simply and quickly carried out on the original image, and corresponding color correction can be carried out on different input original images in a targeted manner, so that the quality of the image can be effectively improved, the contrast of the image is increased, and the image is closer to the real color.
Description
Technical Field
The embodiment of the application relates to the technical field of image processing, in particular to an image color correction method, an image color correction device, a storage medium and a mobile terminal.
Background
With the rapid development of mobile terminals, the quality requirement for images shot by a camera of the mobile terminal is higher and higher. However, the color of the image collected by the camera is closely related to the collection environment, and the colors of the images collected by the same collection target are different in different collection environments. The illumination of the environment and the RGB three components of the image sensor of the camera are collected, and the response to objects of different colors will affect the final imaging color, so in practical application, the color of the image collected by the camera needs to be corrected to restore the true color of the collected target. Therefore, an effective color correction mode becomes important to the quality of the image captured by the camera.
Disclosure of Invention
The embodiment of the application provides an image color correction method, an image color correction device, a storage medium and a mobile terminal, which can effectively improve the quality of an image and enable the image to be closer to a real color.
In a first aspect, an embodiment of the present application provides an image color correction method, including:
acquiring an original image to be processed;
inputting the original image into a pre-trained image color correction model;
and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image.
In a second aspect, an embodiment of the present application provides an image color correction apparatus, including:
the original image acquisition module is used for acquiring an original image to be processed;
the first original image input module is used for inputting the original image into a pre-trained image color correction model;
and the target image determining module is used for determining an output image of the image color correction model and taking the output image as a target image corresponding to the original image.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the image color correction method according to the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a terminal, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the image color correction method according to the first aspect of the embodiment of the present application when executing the computer program.
According to the image color correction scheme provided in the embodiment of the application, an original image to be processed is obtained; inputting the original image into a pre-trained image color correction model; and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image. By adopting the technical scheme, the color correction can be simply and quickly carried out on the original image, and the corresponding color correction can be carried out on different input original images in a targeted manner, so that the quality of the image can be effectively improved, and the image is closer to the real color.
Drawings
Fig. 1 is a schematic flowchart of an image color correction method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flowchart of another image color correction method according to an embodiment of the present disclosure;
FIG. 3 is a schematic flowchart of another image color correction method according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an image color correction apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of another mobile terminal according to an embodiment of the present application.
Detailed Description
The technical scheme of the application is further explained by the specific implementation mode in combination with the attached drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
CCM (Color Correction Matrix) is an important means for performing Color Correction on an image, recovering the Color of the image, adjusting the style of the image, and improving the quality of the image. In the conventional technology, different CCM matrixes are mainly used for images in different scenes, the switching of the images in different scenes easily causes the sudden change of the image effect after color correction, so that the image adjusting effect cannot ensure the consistent style, and especially in the stage of previewing the images by using a camera, the user experience can be greatly influenced in practical application. Based on the above considerations, the following scheme for image color correction is now provided.
Fig. 1 is a flowchart illustrating an image color correction method according to an embodiment of the present application, which may be executed by an image color correction apparatus, where the apparatus may be implemented by software and/or hardware, and may be generally integrated in a mobile terminal. As shown in fig. 1, the method includes:
For example, the mobile terminal in the embodiment of the present application may include a mobile device with a photographing function, such as a mobile phone, a tablet computer, and a camera.
In the embodiment of the application, when it is detected that the camera of the mobile terminal is in an open state, that is, when it is detected that the camera of the mobile terminal is in a shooting preview state or shooting an image, a raw image collected by the camera is acquired, and at this time, the raw image collected by the camera can be used as an original image to be processed. Optionally, the camera acquires a raw image, and performs white balance processing on the raw image based on a preset white balance processing algorithm, so that the raw image after the white balance processing can be used as an original image to be processed. Optionally, raw images or images to be color-corrected transmitted by other terminal devices may also be acquired and taken as original images to be processed. Of course, the image to be color corrected may also be directly obtained from an image library stored in the mobile terminal as the original image to be processed. It should be noted that, in the embodiment of the present application, a source or an obtaining manner of an original image to be processed is not limited.
Optionally, when it is detected that the image color correction event is triggered, the original image to be processed is acquired. It will be appreciated that, in order to perform color correction on an image at an appropriate timing, a trigger condition for an image color correction event may be set in advance. For example, to meet the visual requirements of the user for capturing an image, an image color correction event may be triggered when the camera is detected to be in an on state. Optionally, when the contrast of an image in the mobile terminal is not satisfied by the user, an image color correction event may be triggered when it is detected that the user actively opens the image color correction right. Optionally, in order to apply the image color correction to a more valuable application occasion so as to save additional power consumption caused by the image color correction, the application occasion and the application scene of the image color correction may be analyzed or researched, a reasonable preset scene is set, and when the mobile terminal is detected to be in the preset scene, an image color correction event is triggered. It should be noted that, the embodiment of the present application does not limit the specific representation form in which the image color correction event is triggered.
And 102, inputting the original image into a pre-trained image color correction model.
In the embodiment of the present application, the image color correction model may be understood as a learning model that, after an original image to be processed is input, quickly determines a target image corresponding to the original image to be processed, where the target image corresponding to the original image to be processed is an image obtained by performing image color correction on the original image. The image color correction model may be a learning model generated by training the acquired sample original image and an image color correction image obtained by adjusting the sample original image to the best effect. It is understood that the image color correction model can be generated by learning the sample original image, the image color correction image for adjusting the sample original image to the best effect, and the corresponding relationship between the two. The image color correction model is an end-to-end learning model, i.e., a learning model in which both input and output are images.
And 103, determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image.
For example, after an original image to be processed is input to an image color correction model, the image color correction model analyzes the original image to be processed, performs color correction on the original image according to an analysis result, obtains a target image after the color correction is performed on the original image, and outputs the target image. It can be understood that, after the original image to be processed is input into the image color correction model, and the image color correction model is analyzed, the image is directly output, and then the output image can be used as the target image corresponding to the original image. That is, the output image of the image color correction model is the image obtained by color correcting the original image to be processed by the image color correction model, that is, the target image corresponding to the original image.
The image color correction method provided in the embodiment of the application acquires an original image to be processed; inputting the original image into a pre-trained image color correction model; and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image. By adopting the technical scheme, the color correction can be simply and quickly carried out on the original image, and the corresponding color correction can be carried out on different input original images in a targeted manner, so that the quality of the image can be effectively improved, the contrast of the image is further enhanced, and the image is closer to the real color.
In some embodiments, after the output image is taken as the target image corresponding to the original image, the method further includes: and performing Gamma correction on the target image, and outputting the target image after the Gamma correction. For example, the original image is color-corrected to obtain a target image, and in order to further increase the contrast of the target image, Gamma correction may be further performed on the target image, and the target image after Gamma correction is output. The advantage of this arrangement is that the dark area in the target image can be improved in color, the contrast of the image can be further improved, and the quality of the image can be improved.
Fig. 2 is a schematic flowchart of an image color correction method according to an embodiment of the present application, and as shown in fig. 2, the method includes:
Optionally, acquiring a first sample original image through a camera includes: acquiring a first sample original image of a first standard color card under different illumination through a camera; the first standard color card is a color card; or acquiring first sample original images of at least two shooting scenes under different illumination through a camera.
For example, the first standard color card is a color card, for example, the first standard color card may be a standard color card with 24 pure color blocks of different colors, and then an image of the first standard color card under different illumination is acquired by the camera as a first sample original image. Illustratively, raw images of the first standard color card under different color temperatures are acquired through the camera, and white balance processing is performed on the raw images based on a preset white balance processing algorithm, so that the raw images after the white balance processing can be used as first original sample images.
As another example, images of at least two shooting scenes under different illumination are collected by a camera, and the collected images are taken as a first sample original image. Optionally, at least two shooting scenes preferably contain different colors of shooting objects, so that the shooting scenes are rich in color. It can be understood that different shooting scenes contain different colors of different shooting objects, and then at least two images of the shooting scenes under different illumination are collected through the camera to serve as first original sample images, so that the first original sample images can not only simulate the images of the first standard color cards collected by the camera under different illumination, but also contain more scene information.
In the embodiment of the present application, the color correction may be performed on the first original sample image by using an existing image color correction method, so as to obtain a first sample target image corresponding to the first original sample image. Optionally, the first sample original Image is input into an ISP (Image Signal Processor) tool, and the first sample original Image is adjusted to be color corrected manually, and the Image adjusted to have the best color correction effect is used as the first sample target Image corresponding to the first sample original Image. When the color correction adjustment is performed on the first sample original image, whether the image with the best color correction effect is adjusted or not can be confirmed through the first direct vision of human eyes, and the image with the best color correction effect can be evaluated through the image quality evaluation standard until the image with the best color correction effect is obtained.
Optionally, before performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, the method further includes: collecting a sample RGB image corresponding to the first sample original image through a camera; color correcting the first original sample image to obtain a first target sample image corresponding to the first original sample image, including: and performing color correction on the first sample original image by taking the sample RGB image as a reference image to obtain a first sample target image corresponding to the first sample original image. The method has the advantages that the correction scale of the first original sample image during color correction can be effectively controlled, the color correction of the first original sample image can be simply and quickly completed, and a better color correction effect can be achieved.
Illustratively, when a first original sample image (raw image or image obtained by white balance processing on the raw image) of a first standard color card under different illumination is acquired through a camera, sample RGB images of the first standard color card under different illumination may be acquired through the camera, where the first original sample image and the sample RGB images correspond to each other one to one, that is, the first original sample image and the sample RGB images are both acquired images of the first standard color card under the same illumination. For another example, when the first sample original images (raw images or images obtained by white balance processing on the raw images) of at least two shooting scenes under different illuminations are collected by the camera, sample RGB images of the at least two shooting scenes under different illuminations can be collected by the camera, wherein the first sample original images correspond to the sample RGB images one to one, that is, the first sample original images and the sample RGB images are both collected images of the same shooting scene under the same illumination.
When the color correction is performed on the first sample original image, the sample RGB image corresponding to the first sample original image is used as the reference image, so that a good color correction effect on the first sample original image can be realized. For example, the first sample raw image is input into the ISP tool, and the sample RGB image is used as a reference image, and the first sample raw image is manually color-corrected until the color-corrected image is not much different from the sample RGB image, which may be considered to be an image with better color correction effect.
And 203, taking the first sample original image and the first sample target image as a first training sample set.
And taking the first sample original image and the first sample target image corresponding to the first sample original image as a training sample set of the image color correction model, namely a first training sample set.
And 204, training a first preset machine learning model by using the first training sample set to obtain an image color correction model.
Illustratively, a first pre-set machine learning model is trained using a first set of training samples to generate an image color correction model. The first preset machine learning model may include a convolutional neural network model or a long-term and short-term memory network model, and may further include a naive bayes model. It should be noted that, in the embodiment of the present application, the first preset machine learning model is not limited.
And step 205, acquiring an original image to be processed.
And step 206, inputting the original image into a pre-trained image color correction model.
And step 207, determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image.
Wherein, before obtaining an original image to be processed, an image color correction model is obtained. It should be noted that the mobile terminal may obtain the first training sample set, and train the first preset machine learning model by using the first training sample set to directly generate the image color correction model. For example, before shipping, one mobile terminal is used to obtain the first training sample set and generate the image color correction model, and then the image color correction model is stored in other mobile terminals for direct use by other mobile terminals. Or, the server obtains a large number of first sample original images and first sample target images obtained by performing color correction on the first sample original images to obtain a first training sample set. And the server trains the first training sample set based on the first preset machine learning model to obtain an image color correction model. And when the mobile terminal needs to perform image color correction, calling the trained image color correction model from the server.
The image color correction method provided by the embodiment of the application obtains an original image to be processed, inputs the original image into a pre-trained image color correction model, determines an output image of the image color correction model, and takes the output image as a target image corresponding to the original image, wherein the image color correction model is generated by training a first sample original image and a first sample target image obtained by performing color correction on the first sample original image. By adopting the technical scheme, the first sample original image of the first standard color card collected under different illumination can be effectively utilized, or the first sample original images of at least two shooting scenes collected under different illumination, and the first sample target image after color correction is carried out on the first sample original image, the training and learning of the image color correction model are carried out, the accuracy of the image color correction model can be effectively improved, meanwhile, the color correction can be accurately and quickly carried out on the original image to be processed by utilizing the image color correction model, and the image quality can be effectively improved.
Fig. 3 is a schematic flowchart of an image color correction method according to an embodiment of the present application, and as shown in fig. 3, the method includes:
301, acquiring a first sample original image of a first standard color card under different illumination through a camera; the first standard color card is a color card; or acquiring first sample original images of at least two shooting scenes under different illumination through a camera.
And 304, training a first preset machine learning model by using the first training sample set to obtain an image color correction model.
And 305, acquiring an original image to be processed.
Illustratively, the raw image to be processed includes a raw image captured by a camera. Alternatively, the original image to be processed includes an image that needs color correction, but in order to achieve a better color correction effect, the original image needs to be subjected to white balance processing in advance.
And step 306, inputting the original image into a white balance coefficient matrix determination model trained in advance.
In the embodiment of the present application, the white balance coefficient matrix determination model may be understood as a learning model that, after an original image to be processed is input, quickly determines a white balance coefficient matrix corresponding to the original image to be processed. The white balance coefficient matrix determination model may be a learning model generated by training the acquired second sample original image and a corresponding sample white balance coefficient matrix, where the sample white balance coefficient matrix includes a matrix when the sample original image is adjusted to a best-effect white balance processing image. It can be understood that the white balance coefficient matrix determination model can be generated by learning the second sample original image and the corresponding sample white balance coefficient matrix, and the corresponding relationship between the two.
And 307, determining the output result of the model according to the white balance coefficient matrix, and determining the white balance coefficient matrix corresponding to the original image.
For example, after an original image to be processed is input to the white balance coefficient matrix determination model, the white balance coefficient matrix determination model analyzes the original image, and determines a white balance coefficient matrix corresponding to the original image to be processed according to an analysis result.
And 308, performing white balance processing on the original image according to the white balance coefficient matrix.
For example, the original image to be processed is white-balanced based on the white balance coefficient matrix, and for example, the product of the original image and the white balance coefficient matrix may be used as the image after the original image is white-balanced.
Optionally, performing white balance processing on the original image according to the white balance coefficient matrix, including: acquiring a first RGB component value of each pixel point in the original image; and aiming at all pixel points in the original image, taking the product of the first RGB component value of each pixel point and the white balance coefficient at the corresponding position in the white balance coefficient matrix as the second RGB component value of the pixel point of the target image corresponding to the pixel point of the original image. The method has the advantages that an independent white balance coefficient can be determined for each pixel point in the original image to be processed, white balance processing is carried out on each pixel point in the original image based on the white balance coefficient matrix, the technical problems that when white balance processing is carried out based on a global white balance algorithm, color deviation of a pure color object is large easily, and a white block cannot be accurately detected at a mixed color temperature can be solved, the quality of the image can be effectively improved, and the saturation of the image is increased.
Illustratively, a first RGB component value of each pixel point in the original image is obtained, and for all pixel points in the original image, the first RGB component value of each pixel point is multiplied by a white balance coefficient at a corresponding position in a white balance coefficient matrix, and a result after the multiplication is used as a second RGB component value of a pixel point of the target image corresponding to the pixel point in the original image, that is, the result after the multiplication is used as a second RGB component value of the pixel point after the white balance processing is performed on the original image. Illustratively, a first RGB component value of a first pixel point (a pixel point in a first row and a first column in the original image) in the original image is obtained, and a product of a white balance coefficient in the first row and the first column in the white balance coefficient matrix and the first RGB component value of the first pixel point is used as a second RGB component value of the first pixel point (a pixel point in the first row and the first column in the white balance processed image) in the image after the white balance processing is performed on the original image. And analogizing in sequence, and performing similar processing operation on each pixel point in the original image based on the white balance coefficient matrix, thereby obtaining the image after the white balance processing is performed on the original image.
In the embodiment of the present application, the original image after the white balance processing is input into the image color correction model, and the image color correction model analyzes the image to perform color correction.
And step 310, determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image.
And 311, performing Gamma correction on the target image, and outputting the target image after the Gamma correction.
The image color correction method provided by the embodiment of the application obtains an original image to be processed, inputs the original image into a white balance coefficient matrix determination model which is trained in advance, determines a white balance coefficient matrix corresponding to the original image according to an output result of the white balance coefficient matrix determination model, performs white balance processing on the original image according to the white balance coefficient matrix, inputs the original image which is subjected to the white balance processing into the image color correction model which is trained in advance, determines an output image of the image color correction model, and takes the output image as a target image corresponding to the original image. By adopting the technical scheme, the white balance processing can be carried out on the original image by utilizing the white balance coefficient matrix determination model, and the color correction can be carried out on the image after the white balance processing by utilizing the image color correction model, so that the contrast of the original image can be improved, the saturation of the image can be improved, and the image quality can be effectively improved.
In some embodiments, before inputting the original image into the white balance coefficient matrix determination model trained in advance, the method further includes: acquiring a white balance coefficient matrix determining model; wherein the white balance coefficient matrix determination model is obtained by: acquiring a second sample original image of a second standard color card under different color temperatures through a camera; wherein the second standard color card is a white color card; performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image; determining a sample white balance coefficient matrix corresponding to the second sample original image and the second sample target image according to the second sample original image and the second sample target image; marking the second sample original image according to the sample white balance coefficient matrix to obtain a second training sample set; and training a second preset machine learning model by using the second training sample set to obtain a white balance coefficient matrix determination model.
In the embodiment of the present application, the second standard color card is a white color card, and images of the second standard color card at different color temperatures are collected by the camera and are used as the second sample original image. Illustratively, raw images of standard color cards at different color temperatures are collected by a camera as a second sample original image. Different color temperatures can be achieved by artificial light sources, for example, in a laboratory environment, different color temperature environments are created by different types of light sources. For example, a candle may be used as a light source to create a 2000k color temperature environment, a high pressure sodium lamp may be used as a light source to create a 1950-. A series of shooting environments with continuous color temperature values can be provided through different types of light sources. And shooting a second standard color card by using the camera at different color temperatures to obtain a color card image at each color temperature, thereby obtaining a second sample original image of the second standard color card at different color temperatures.
For example, the second sample original image may be subjected to white balance processing using an existing white balance processing method, so as to obtain a second sample target image corresponding to the second sample original image. Optionally, the second sample original image is input into the ISP tool, the white balance adjustment is manually performed on the second sample original image, and the image adjusted to the best white balance effect is used as the second sample target image corresponding to the second sample original image. When the white balance adjustment is performed on the second sample original image, whether the image with the best white balance effect is adjusted or not can be confirmed through the second visual perception of human eyes, and the image with the best white balance effect can be evaluated through the image quality evaluation standard until the image with the best white balance effect is obtained.
In the embodiment of the present application, according to the second sample original image and the second sample target image corresponding to the second sample original image, when the second sample original image is changed into the second sample target image, a corresponding sample white balance coefficient matrix is determined, that is, a white balance coefficient matrix adopted in a white balance processing process when the second sample original image is subjected to white balance processing to obtain the second sample target image is determined.
Optionally, determining, according to the second sample original image and the second sample target image, a sample white balance coefficient matrix corresponding to the second sample original image which is changed into the second sample target image includes: acquiring a third RGB component value of each pixel point in the second sample original image and a fourth RGB component value of each pixel point in the second sample target image; and regarding all the pixel points, taking the ratio of the fourth RGB component value and the third RGB component value corresponding to each pixel point as a white balance coefficient corresponding to the pixel point in the sample white balance coefficient matrix. The method has the advantage that the corresponding white balance coefficient matrix can be accurately determined when the white balance processing is carried out on the original image of the second standard color card under the environment with different color temperatures.
Illustratively, a third RGB component value of each pixel point in the second sample original image and a fourth RGB component value of each pixel point in the second sample target image are respectively obtained, for each pixel point, a ratio of the fourth RGB component value to the third RGB component value of the corresponding pixel point is respectively calculated, and the ratio is used as a white balance coefficient matrix of the pixel point. Illustratively, a third RGB component value of a first pixel point (a pixel point in a first row and a first column in the second sample original image) in the second sample original image and a fourth RGB component value of a first pixel point (a pixel point in a first row and a first column in the first sample target image) in the second sample target image are obtained, and a ratio of the fourth RGB component value to the third RGB component value is used as a white balance coefficient in a first row and a first column in a white balance coefficient matrix. And respectively determining the white balance coefficients of all the elements in the white balance coefficient matrix by analogy in the manner described above.
Illustratively, the corresponding second sample original images are respectively marked according to the obtained white balance coefficient matrix of each sample, and the marked second sample original images of the white balance coefficient matrix of the corresponding sample are used as a training sample set of the white balance coefficient matrix determination model, that is, a second training sample set. Illustratively, a second pre-set machine learning model is trained using a second set of training samples to generate a white balance coefficient matrix determination model. The second preset machine learning model may include a convolutional neural network model or a long-term memory network model. The second preset machine learning model is not limited in the embodiment of the application, wherein the second preset machine learning model and the first preset machine learning model can be the same or different.
Before an original image to be processed is obtained, a white balance coefficient matrix determining model is obtained. It should be noted that the mobile terminal may obtain the second training sample set, train the second preset machine learning model by using the second training sample set, and directly generate the white balance coefficient matrix determination model. The mobile terminal may also directly invoke a white balance coefficient matrix determination model generated by training of other mobile terminals, for example, before shipment, one mobile terminal is used to obtain a second training sample set and generate the white balance coefficient matrix determination model, and then the white balance coefficient matrix determination model is stored in other mobile terminals for direct use by other mobile terminals. Or the server acquires a large number of second sample original images and white balance coefficient matrixes corresponding to the second sample original images, and marks the second sample original images according to the corresponding white balance coefficient matrixes to obtain a second training sample set. And the server trains a second training sample set based on a second preset machine learning model to obtain a white balance coefficient matrix determination model. And when the mobile terminal needs to perform image white balance processing, calling the trained white balance coefficient matrix from the server to determine the model.
Fig. 4 is a schematic structural diagram of an image color correction apparatus provided in an embodiment of the present application, where the apparatus may be implemented by software and/or hardware, and is generally integrated in a mobile terminal, and may perform color correction on an original image to be processed by executing an image color correction method. As shown in fig. 4, the apparatus includes:
an original image obtaining module 401, configured to obtain an original image to be processed;
a first original image input module 402, configured to input the original image into a pre-trained image color correction model;
a target image determining module 403, configured to determine an output image of the image color correction model, and use the output image as a target image corresponding to the original image.
The image color correction device provided in the embodiment of the application acquires an original image to be processed; inputting the original image into a pre-trained image color correction model; and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image. By adopting the technical scheme, the color correction can be simply and quickly carried out on the original image, and the corresponding color correction can be carried out on different input original images in a targeted manner, so that the quality of the image can be effectively improved, and the image is closer to the real color.
Optionally, the apparatus further comprises:
the color correction model acquisition module is used for acquiring the image color correction model before acquiring an original image to be processed;
wherein the image color correction model is obtained by:
acquiring a first sample original image through a camera;
carrying out color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;
taking the first sample original image and the first sample target image as a first training sample set;
and training a first preset machine learning model by using the first training sample set to obtain an image color correction model.
Optionally, before performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, the method further includes:
collecting a sample RGB image corresponding to the first sample original image through a camera;
color correcting the first original sample image to obtain a first target sample image corresponding to the first original sample image, including:
and performing color correction on the first sample original image by taking the sample RGB image as a reference image to obtain a first sample target image corresponding to the first sample original image.
Optionally, acquiring a first sample original image through a camera includes:
acquiring a first sample original image of a first standard color card under different illumination through a camera; the first standard color card is a color card; or
The method comprises the steps of collecting first sample original images of at least two shooting scenes under different illumination through a camera.
Optionally, the apparatus further comprises:
the second original image input module is used for inputting the original image into a pre-trained white balance coefficient matrix determination model before inputting the original image into the pre-trained image color correction model;
the white balance coefficient matrix determining module is used for determining a white balance coefficient matrix corresponding to the original image according to an output result of the white balance coefficient matrix determining model;
the white balance processing module is used for carrying out white balance processing on the original image according to the white balance coefficient matrix;
the first original image input module is configured to:
and inputting the original image subjected to white balance processing into a pre-trained image color correction model.
Optionally, the apparatus further comprises:
the coefficient matrix determination model acquisition module is used for acquiring a white balance coefficient matrix determination model before the original image is input into a pre-trained white balance coefficient matrix determination model;
wherein the white balance coefficient matrix determination model is obtained by:
acquiring a second sample original image of a second standard color card under different color temperatures through a camera; wherein the second standard color card is a white color card;
performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image;
determining a sample white balance coefficient matrix corresponding to the second sample original image and the second sample target image according to the second sample original image and the second sample target image;
marking the second sample original image according to the sample white balance coefficient matrix to obtain a second training sample set;
and training a second preset machine learning model by using the second training sample set to obtain a white balance coefficient matrix determination model.
Optionally, the apparatus further comprises:
and the Gamma correction module is used for performing Gamma correction on the target image after the output image is taken as the target image corresponding to the original image, and outputting the target image after the Gamma correction.
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method of image color correction, the method comprising:
acquiring an original image to be processed;
inputting the original image into a pre-trained image color correction model;
and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDRRAM, SRAM, EDORAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the image color correction operation described above, and may also perform related operations in the image color correction method provided in any embodiments of the present application.
The embodiment of the application provides a mobile terminal, and the image color correction device provided by the embodiment of the application can be integrated in the mobile terminal. Fig. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application. The mobile terminal 500 may include: the image color correction system comprises a memory 501, a processor 502 and a computer program stored on the memory and executable by the processor, wherein the processor 502 implements the image color correction method according to the embodiment of the application when executing the computer program.
The mobile terminal provided by the embodiment of the application can simply and quickly carry out color correction on the original image, and can also carry out corresponding color correction on different input original images in a targeted manner, so that the quality of the image can be effectively improved, and the image is closer to the real color.
Fig. 6 is a schematic structural diagram of another mobile terminal provided in an embodiment of the present application, where the mobile terminal may include: a housing (not shown), a memory 601, a Central Processing Unit (CPU) 602 (also called a processor, hereinafter referred to as CPU), a circuit board (not shown), and a power circuit (not shown). The circuit board is arranged in a space enclosed by the shell; the CPU602 and the memory 601 are disposed on the circuit board; the power supply circuit is used for supplying power to each circuit or device of the mobile terminal; the memory 601 is used for storing executable program codes; the CPU602 executes a computer program corresponding to the executable program code by reading the executable program code stored in the memory 601 to implement the steps of:
acquiring an original image to be processed;
inputting the original image into a pre-trained image color correction model;
and determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image.
The mobile terminal further includes: peripheral interface 603, RF (Radio Frequency) circuitry 605, audio circuitry 606, speakers 611, power management chip 608, input/output (I/O) subsystem 609, other input/control devices 610, touch screen 612, other input/control devices 610, and external port 604, which communicate via one or more communication buses or signal lines 607.
It should be understood that the illustrated mobile terminal 600 is merely one example of a mobile terminal and that the mobile terminal 600 may have more or fewer components than shown, may combine two or more components, or may have a different configuration of components. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.
The following describes in detail the mobile terminal for image color correction provided in this embodiment, which is exemplified by a mobile phone.
A memory 601, the memory 601 being accessible by the CPU602, the peripheral interface 603, and the like, the memory 601 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other volatile solid state storage devices.
A peripheral interface 603, said peripheral interface 603 may connect input and output peripherals of the device to the CPU602 and the memory 601.
An I/O subsystem 609, the I/O subsystem 609 may connect input and output peripherals on the device, such as a touch screen 612 and other input/control devices 610, to the peripheral interface 603. The I/O subsystem 609 may include a display controller 6091 and one or more input controllers 6092 for controlling other input/control devices 610. Where one or more input controllers 6092 receive electrical signals from or transmit electrical signals to other input/control devices 610, the other input/control devices 610 may include physical buttons (push buttons, rocker buttons, etc.), dials, slide switches, joysticks, click wheels. It is noted that the input controller 6092 may be connected to any one of: a keyboard, an infrared port, a USB interface, and a pointing device such as a mouse.
A touch screen 612, which touch screen 612 is an input interface and an output interface between the user's mobile terminal and the user, displays visual output to the user, which may include graphics, text, icons, video, and the like.
The display controller 6091 in the I/O subsystem 609 receives electrical signals from the touch screen 612 or transmits electrical signals to the touch screen 612. The touch screen 612 detects a contact on the touch screen, and the display controller 6091 converts the detected contact into an interaction with a user interface object displayed on the touch screen 612, that is, to implement a human-computer interaction, where the user interface object displayed on the touch screen 612 may be an icon for running a game, an icon networked to a corresponding network, or the like. It is worth mentioning that the device may also comprise a light mouse, which is a touch sensitive surface that does not show visual output, or an extension of the touch sensitive surface formed by the touch screen.
The RF circuit 605 is mainly used to establish communication between the mobile phone and the wireless network (i.e., network side), and implement data reception and transmission between the mobile phone and the wireless network. Such as sending and receiving short messages, e-mails, etc. In particular, RF circuitry 605 receives and transmits RF signals, also referred to as electromagnetic signals, through which RF circuitry 605 converts electrical signals to or from electromagnetic signals and communicates with a communication network and other devices. RF circuitry 605 may include known circuitry for performing these functions including, but not limited to, an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC (CODEC) chipset, a Subscriber Identity Module (SIM), and so forth.
The audio circuit 606 is mainly used to receive audio data from the peripheral interface 603, convert the audio data into an electric signal, and transmit the electric signal to the speaker 611.
The speaker 611 is used to convert the voice signal received by the handset from the wireless network through the RF circuit 605 into sound and play the sound to the user.
And a power management chip 608 for supplying power and managing power to the hardware connected to the CPU602, the I/O subsystem, and the peripheral interface.
The image color correction device, the storage medium and the mobile terminal provided in the above embodiments can execute the image color correction method provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing the method. For details of the image color correction method provided in any of the embodiments of the present application, reference may be made to the technical details not described in detail in the above embodiments.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (7)
1. An image color correction method, comprising:
acquiring an original image to be processed;
inputting the original image into a pre-trained image color correction model;
determining an output image of the image color correction model, and taking the output image as a target image corresponding to the original image;
before acquiring an original image to be processed, the method further comprises the following steps:
acquiring the image color correction model;
wherein the image color correction model is obtained by:
acquiring first sample original images of at least two shooting scenes under different illumination through a camera;
carrying out color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;
taking the first sample original image and the first sample target image as a first training sample set;
training a first preset machine learning model by using the first training sample set to obtain an image color correction model;
before inputting the original image into a pre-trained image color correction model, the method further comprises:
inputting the original image into a white balance coefficient matrix determination model trained in advance;
determining an output result of a model according to the white balance coefficient matrix, and determining a white balance coefficient matrix corresponding to the original image;
carrying out white balance processing on the original image according to the white balance coefficient matrix;
inputting the original image into a pre-trained image color correction model, comprising:
inputting the original image after white balance processing into a pre-trained image color correction model;
performing white balance processing on the original image according to the white balance coefficient matrix, including: acquiring a first RGB component value of each pixel point in the original image; and aiming at all pixel points in the original image, taking the product of the first RGB component value of each pixel point and the white balance coefficient at the corresponding position in the white balance coefficient matrix as the second RGB component value of the pixel point of the target image corresponding to the pixel point of the original image.
2. The method according to claim 1, before performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, further comprising:
collecting a sample RGB image corresponding to the first sample original image through a camera;
color correcting the first original sample image to obtain a first target sample image corresponding to the first original sample image, including:
and performing color correction on the first sample original image by taking the sample RGB image as a reference image to obtain a first sample target image corresponding to the first sample original image.
3. The method of claim 1, further comprising, before inputting the raw image into a pre-trained white balance coefficient matrix determination model:
acquiring a white balance coefficient matrix determining model;
wherein the white balance coefficient matrix determination model is obtained by:
acquiring a second sample original image of a second standard color card under different color temperatures through a camera; wherein the second standard color card is a white color card;
performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image;
determining a sample white balance coefficient matrix corresponding to the second sample original image and the second sample target image according to the second sample original image and the second sample target image;
marking the second sample original image according to the sample white balance coefficient matrix to obtain a second training sample set;
and training a second preset machine learning model by using the second training sample set to obtain a white balance coefficient matrix determination model.
4. The method according to any one of claims 1 to 3, further comprising, after taking the output image as a target image corresponding to the original image:
and performing Gamma correction on the target image, and outputting the target image after the Gamma correction.
5. An image color correction apparatus, comprising:
the original image acquisition module is used for acquiring an original image to be processed;
the first original image input module is used for inputting the original image into a pre-trained image color correction model;
a target image determining module, configured to determine an output image of the image color correction model, and use the output image as a target image corresponding to the original image;
wherein the apparatus further comprises:
the color correction model acquisition module is used for acquiring the image color correction model before acquiring an original image to be processed;
wherein the image color correction model is obtained by:
acquiring first sample original images of at least two shooting scenes under different illumination through a camera;
carrying out color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;
taking the first sample original image and the first sample target image as a first training sample set;
training a first preset machine learning model by using the first training sample set to obtain an image color correction model;
wherein the apparatus further comprises:
the second original image input module is used for inputting the original image into a pre-trained white balance coefficient matrix determination model before inputting the original image into the pre-trained image color correction model;
the white balance coefficient matrix determining module is used for determining a white balance coefficient matrix corresponding to the original image according to an output result of the white balance coefficient matrix determining model;
the white balance processing module is used for carrying out white balance processing on the original image according to the white balance coefficient matrix;
the first original image input module is configured to:
inputting the original image after white balance processing into a pre-trained image color correction model;
the white balance processing module is used for acquiring a first RGB component value of each pixel point in the original image; and aiming at all pixel points in the original image, taking the product of the first RGB component value of each pixel point and the white balance coefficient at the corresponding position in the white balance coefficient matrix as the second RGB component value of the pixel point of the target image corresponding to the pixel point of the original image.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the image color correction method according to any one of claims 1 to 4.
7. A mobile terminal, characterized in that it comprises a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the image color correction method according to any one of claims 1 to 4 when executing said computer program.
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| PCT/CN2019/107580 WO2020103570A1 (en) | 2018-11-19 | 2019-09-24 | Image color correction method, device, storage medium and mobile terminal |
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| CN110211065B (en) * | 2019-05-23 | 2023-10-20 | 九阳股份有限公司 | Color correction method and device for food material image |
| CN110428377B (en) * | 2019-07-26 | 2023-06-30 | 北京康夫子健康技术有限公司 | Data expansion method, device, equipment and medium |
| CN110796642A (en) * | 2019-10-09 | 2020-02-14 | 陈浩能 | Method for determining fruit quality degree and related product |
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