CN110248107A - Image processing method and device - Google Patents
Image processing method and device Download PDFInfo
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- CN110248107A CN110248107A CN201910509690.6A CN201910509690A CN110248107A CN 110248107 A CN110248107 A CN 110248107A CN 201910509690 A CN201910509690 A CN 201910509690A CN 110248107 A CN110248107 A CN 110248107A
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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
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/71—Circuitry for evaluating the brightness variation
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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
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/73—Circuitry for compensating brightness variation in the scene by influencing the exposure time
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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
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/741—Circuitry for compensating brightness variation in the scene by increasing the dynamic range of the image compared to the dynamic range of the electronic image sensors
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
- H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
- H04N25/62—Detection or reduction of noise due to excess charges produced by the exposure, e.g. smear, blooming, ghost image, crosstalk or leakage between pixels
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Abstract
The application proposes a kind of image processing method and device, wherein method includes: the multiframe original image of photographic subjects object；The corresponding target area of target object in every frame original image in detection multiframe original image；Noise reduction based on artificial intelligence is carried out to the target area of every frame original image and generates noise-reduced image；High dynamic is carried out to all noise-reduced images and is synthetically generated target image.Thus, on the one hand, using the noise reduction mode based on artificial intelligence to image noise reduction, remain more image details while guaranteeing image degree of purity, on the other hand, carry out noise reduction process only for target area, substantially increase the efficiency of noise reduction.
Description
Technical field
This application involves technical field of imaging more particularly to a kind of image processing methods and device.
Background technique
With the development of science and technology, the camera technology for relying on science and technology is more and more mature, in daily production and life
In, take pictures using the built-in camera of intelligent mobile terminal (such as smart phone, tablet computer), it is a kind of normal to have become
State.Therefore, as the normalization for the demand taken pictures develops, the quality for how improving image becomes major demands.
In the related technology, the raising to picture quality is realized based on the noise reduction algorithm to whole image, for example, being based on whole
Image progress noise reduction process calculation amount is larger, especially when multiple image is synthesized for high dynamic, carries out to multiple image equal
The data processing amount for carrying out whole noise reduction is bigger, therefore, needs a kind of mode of processing speed for improving noise reduction.
Summary of the invention
The application provides a kind of image processing method and device, to solve in the prior art, the slow technology of noise reduction
Problem.
The application one side embodiment provides a kind of image processing method, comprising the following steps: photographic subjects object
Multiframe original image；Detect the corresponding target area of target object described in every frame original image in the multiframe original image；
Noise reduction based on artificial intelligence is carried out to the target area of every frame original image and generates noise-reduced image；To all noise reduction figures
Target image is synthetically generated as carrying out high dynamic.
The application another aspect embodiment provides a kind of image processing apparatus, comprising: shooting module is used for photographic subjects
The multiframe original image of object；Detection module, for detecting target described in every frame original image in the multiframe original image
The corresponding target area of object；Noise reduction module is carried out for the target area to every frame original image based on artificial intelligence
Noise reduction generate noise-reduced image；Synthesis module is synthetically generated target image for carrying out high dynamic to all noise-reduced images.
The another aspect embodiment of the application provides a kind of electronic equipment, comprising: imaging sensor, memory, processor
And the computer program that can be run on a memory and on a processor is stored, described image sensor is electrically connected with the processor
It connects, when the processor executes described program, realizes the image processing method as described in above-described embodiment.
The application also one side embodiment provides a kind of computer readable storage medium, is stored thereon with computer journey
Sequence realizes the image processing method as described in above-described embodiment when the computer program is executed by processor.
Image processing method embodiment provided by the present application includes at least following advantageous effects:
The multiframe original image of photographic subjects object detects in multiframe original image target object pair in every frame original image
The target area answered carries out the noise reduction based on artificial intelligence to the target area of every frame original image and generates noise-reduced image, in turn,
High dynamic is carried out to all noise-reduced images and is synthetically generated target image.Thus, on the one hand, use the noise reduction based on artificial intelligence
Mode remains more image details while guaranteeing image degree of purity to image noise reduction, on the other hand, only for mesh
It marks region and carries out noise reduction process, substantially increase the efficiency of noise reduction.
The additional aspect of the application and advantage will be set forth in part in the description, and will partially become from the following description
It obtains obviously, or recognized by the practice of the application.
Detailed description of the invention
The application is above-mentioned and/or additional aspect and advantage will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 is a kind of flow diagram of image processing method provided by the embodiment of the present application；
Fig. 2 is a kind of application scenarios schematic diagram of image processing method provided by the embodiment of the present application；
Fig. 3 is the application scenarios schematic diagram of another kind image processing method provided by the embodiment of the present application；
Fig. 4 is the application scenarios schematic diagram of another image processing method provided by the embodiment of the present application；
Fig. 5 is the flow diagram of another kind image processing method provided by the embodiment of the present application；
Fig. 6 is the structural schematic diagram according to the image processing apparatus of the application one embodiment；
Fig. 7 is the structural schematic diagram of a kind of electronic equipment provided by the embodiments of the present application.
Specific embodiment
Embodiments herein is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the application, and should not be understood as the limitation to the application.
For what is mentioned in above-mentioned background technique, the lower problem of noise reduction process efficiency, this application provides one kind to figure
The noise level of picture is identified, is carried out noise reduction to the biggish image of noise level, is substantially increased the effect of noise reduction process as a result,
Rate.
The application in order to facilitate understanding is right first before the image processing method to the embodiment of the present application is illustrated
This application involves the meaning that is used for of technology be defined, be defined as follows:
Light exposure: it can be appreciated that exposure value (Exposure Value, abbreviation EV), in the initial definition of exposure value
In, exposure value does not imply that an accurate numerical value, and referring to " can provide all camera apertures of identical light exposure and expose
The combination of light time length ".Sensitivity, aperture and exposure time have determined that the light exposure of camera, different parameter combinations can produce
The EV value of equal light exposure, i.e. these various combinations is the same, for example, using 1/125 in the identical situation of sensitivity
The aperture combination of second exposure time and F/11, with the combination for using 1/250 second time for exposure and F/8.0 shutter, the exposure of acquisition
Amount be it is identical, i.e., EV value is identical.EV value be 0 when, refer to sensitivity be 100, aperture-coefficient F/1, exposure time 1
The light exposure obtained when the second；Light exposure increases by one grade, i.e., exposure time doubles or sensitivity doubles, Huo Zheguang
Circle increases by one grade, and EV value increases by 1, that is to say, that the corresponding light exposure of 1EV is twice of the corresponding light exposure of 0EV.Such as 1 institute of table
Show, when individually changing for exposure time, aperture, sensitivity, the corresponding relationship with EV value.
Table 1
Camera work entered after the digital age, and the light measuring function of camera internal is very powerful, and EV is then through common
Differential to indicate to expose one on scale, many cameras all allow that exposure compensating is arranged, and are usually indicated with EV.This
In the case of, EV refers to that the difference of camera photometering data corresponding light exposure and actual exposure amount, such as the exposure compensating of+1EV are
Refer to and increase by one grade of exposure relative to the corresponding light exposure of camera photometering data, is i.e. actual exposure amount is that camera photometering data are corresponding
Twice of light exposure.
In the embodiment of the present application, the determining corresponding EV value of benchmark light exposure can be preset as 0 ,+1EV refers to increase
One grade of exposure, i.e. light exposure are 2 times of benchmark light exposure, and+2EV refers to two grades of exposures of increase, i.e. light exposure is benchmark light exposure
4 times, -1EV refers to one grade of exposure of reduction, i.e. light exposure is 0.5 times etc. of benchmark light exposure.
For example, if the quantity of multiple image is 7 frames, the corresponding EV value range of preset exposure compensating strategy can be with
It is [+1 ,+1 ,+1 ,+1,0, -3, -6].Wherein, exposure compensating strategy is the frame of+1EV, can solve noise problem, passes through brightness
Relatively high frame carries out time domain noise reduction, inhibits noise while promoting dark portion details；Exposure compensating strategy is the frame of -6EV, can
To solve the problems, such as bloom overexposure, retain the details of highlight area；Exposure compensating strategy is the frame of 0EV and -3EV, then can use
In holding bloom to the transition between dark space, the effect of preferable light and shade transition is kept.
Below with reference to the accompanying drawings the image processing method and device of the embodiment of the present application are described.
Fig. 1 is a kind of flow diagram of image processing method provided by the embodiment of the present application.As shown in Figure 1, the party
Method the following steps are included:
Step 101, the multiframe original image of photographic subjects object.
Wherein, target object refers to the main body of any shooting, it is believed that face, or building etc., wherein
Can be discovered whether based on the structure light modulation pattern of the scene of shooting current shooting image be all comprising target object, can also
Target object is taken with the image recognition technology guarantee based on preview image.
Specifically, the multiframe original image of photographic subjects object, wherein original image refers to the figure by electronic equipment
The RAW image for not doing any processing collected as sensor, wherein RAW image is exactly that imaging sensor will capture
Light signal is converted into the original image of digital signal.RAW image has recorded the collected raw information of digital camera sensor,
It has recorded simultaneously and generated some metadata is shot by camera, such as the setting of sensitivity, shutter speed, f-number, white balance
Deng.
It is appreciated that in order to make up the drawbacks of acquisition image can not clearly be presented each details in image,
Taken pictures using high dynamic range images (High-Dynamic Range, abbreviation HDR) technology, i.e., according to different exposures when
Between shoot multiple images, and synthesized based on multiple images to provide more dynamic ranges and image detail, in the application
Embodiment in, equally based on multiple original images carry out high dynamic range be synthetically generated, to guarantee last imaging effect.
In one embodiment of the application, in order to guarantee the picture quality after high dynamic range encloses, as shown in Fig. 2,
The original image for acquiring different light exposures is synthesized, wherein light exposure refers to the sensor devices in electronic equipment in exposure
The number of light is received in length, light exposure is related with aperture, exposure time and sensitivity.Wherein, aperture i.e. clear aperture,
Determine the quantity that light passes through in the unit time；Exposure time refers to time of the light by camera lens；Sensitivity, also known as ISO
Value is to measure egative film for the index of the sensitivity level of light, for indicating the film speed of photosensitive element, ISO numerical value more it is high just
Illustrate that the photoperceptivity of the sensitive component is stronger, the image that the original image of different light exposures can retain under different brightness is thin
Section, the original image of different light exposures compensates mutually image detail as a result, improves the quality for the image being finally synthesizing.
In the present embodiment, when shooting original image, acquisition and ambient light intensity, for example, being based on light sensor
The brightness of detection determines that ambient light intensity for another example determines ambient light intensity, in turn, base according to the photosensitive parameter such as ISO value
Benchmark light exposure is determined in ambient light intensity, and at least first original image of frame, acquisition is acquired according to benchmark light exposure and is lower than
At least second original image of frame for benchmark light exposure, and acquire at least frame third original graph for being higher than benchmark light exposure
Picture.Different light exposures ensure that the image detail of different brightness as a result,.In the embodiment of the present application, benchmark light exposure refers to and passes through
Preview image is carried out after surveying the luminance information for the present filming scene that light obtains, the luminance information phase with current environment determined
The light exposure of adaptation, the value of benchmark light exposure can be the product between benchmark sensitivity and benchmark exposure time.
Wherein, the mode for obtaining the number of image frames of benchmark light exposure is different, as a kind of possible example,
Preview image can be obtained first, specifically, benchmark light exposure can be determined according to the image quality of preview image
Number of image frames n, meet the n frame original image of benchmark light exposure to acquire, and acquire at least frame for being lower than benchmark light exposure
Original image.Wherein, n is the natural number more than or equal to 1.It should be noted that when the number of image frames of acquisition is more, it is entire to clap
Taking the photograph duration can be too long, is easier in shooting process by external interference, therefore in the embodiment of the present application, number of image frames n's is taken
Value may range from 3 or 4, to reduce shooting duration of video, obtain the first original image of better quality.
It specifically, can be by the float degree of preview image, further, it is also possible to the signal-to-noise ratio for passing through preview image
The image quality of preview image is measured, and then according to the image quality of preview image, determines the number of image frames n of benchmark light exposure.
If the image quality of preview image is higher, estimate in shooting process that extraneous annoyance level is lighter, and shooting duration of video can be appropriately extended,
Acquire more multiframe original image；Conversely, estimating extraneous annoyance level in shooting process if the image quality of preview image is poorer
Larger, shooting duration of video can suitably shorten, and acquire less the first original image of frame.
For example, the displacement sensor that can be arranged by electronic equipment, collects imaging sensor in shooting process
Displacement information, and then the current degree of jitter of imaging sensor is determined according to the displacement information of acquisition, is weighed according to degree of jitter
Image quality is measured, and then determines that photographed scene is the lighter biggish hand-held mould of foot prop mode or degree of jitter of degree of jitter
Formula.
It, can be according to setting when acquisition is lower than an at least frame original image for benchmark light exposure in the embodiment of the present application
Exposure compensating grade, benchmark exposure time is compensated, obtains the compensation exposure time less than benchmark exposure time, in turn
According to compensation exposure time and benchmark sensitivity, an at least frame original image is acquired.
It is to be understood that taking different exposure compensatings respectively to an at least frame original image by exposure compensating grade
Strategy, so that image to be collected corresponds to different light exposures, to obtain the image with Different Dynamic range.
It should be noted that light exposure does not imply that an accurate numerical value, and refers in the initial definition of light exposure
" all camera apertures and the combination of exposure time that identical light exposure can be provided ".Sensitivity, aperture and exposure time are true
The light exposure of camera is determined, different parameter combinations can produce equal light exposure.Exposure compensating grade be to light exposure into
The parameter of row adjustment, so that certain images are under-exposure, certain image overexposures, it is also possible that certain image appropriate exposures.This
Apply in embodiment, the corresponding exposure compensating grade value range of an at least frame original image can be EV-5 to EV+5.
As an example, acquisition is lower than at least second original image of frame for benchmark light exposure, for example, original for two frames
Image, two frame original images correspond to different exposure compensating grades at this time, and the exposure compensating grade of two frame original images is less than
EV0.Specifically, benchmark exposure time is compensated according to two frame original images corresponding exposure compensating grade, is less than
The compensation exposure time of benchmark exposure time, in turn, according to compensation exposure time and benchmark sensitivity, two frames second of acquisition are original
Image.
Acquisition is higher than an at least frame third original image for benchmark light exposure, for example, being two frame original images, at this time two frame
Original image corresponds to different exposure compensating grades, and the exposure compensating grade of two frame original images is greater than EV0.Specifically, root
Benchmark exposure time is compensated according to two frame original images corresponding exposure compensating grade, is obtained greater than benchmark exposure time
Exposure time is compensated, in turn, according to compensation exposure time and benchmark sensitivity, acquires two frame third original images.
Obviously, the gradient of the brightness adjustment of image is more, and obtained original image can more fully go back the thin of original image
Section, however under some scenes, for example, containing darker region in the scene, then obviously using the exposure compensating for owing to expose
It measures not high to the presentation positive influence of image detail, for another example, contains brighter region in the scene, then obvious overexposure
Exposure compensation amount is not also high to the presentation positive influence of image detail, therefore, in order to improve image processing efficiency, the application's
In embodiment, exposure compensating grade is determined based on ambient brightness, original image is shot based on exposure compensating grade, wherein the exposure
Light compensates grade as shown in figure 3, may be only EV0 and EV+, further improves image processing efficiency.
Certainly, in order to look after the human face region in image, the experience of taking pictures of user is further promoted, also detectable preview
Whether image detection includes human face region, if determining face screening-mode according to ambient light intensity comprising human face region,
For example, when ambient light intensity corresponds to face on daytime photographing mode, it is determined that when comprising human face region, the screening-mode
Corresponding light exposure adjusted value adjusts the benchmark light exposure obtained in example one according to the light exposure adjusted value, is guaranteeing as a result,
On the basis of the details of whole image saves, the quality that the details of human face region retains has been taken into account.Wherein, the exposure in this example
Light quantity adjusted value can be previously according to the storage setting corresponding with screening-mode of shooting hardware parameter.
For example, when the benchmark light exposure determined according to ambient light intensity is EV-5, if detecting preview image
In include facial image, then according to ambient light intensity determine current shooting mode be night scene face screening-mode, thus, consider
It is brighter relative to other regions to night scene face screening-mode human face region, it may be retained more using lower light exposure
Benchmark light exposure is adjusted to EV-5.5 accordingly, it is determined that reducing by 0.5 light exposure (light exposure adjusted value) by highlights image detail.
Step 102, the corresponding target area of target object in every frame original image is detected in multiframe original image.
It specifically, can be based on target object pair in every frame original image in image recognition algorithm detection multiframe original image
The target area answered targetedly is handled in order to subsequent for target area.
The human face region of every frame original image is detected when target object is face as a kind of possible example, such as
Human face region is determined based on the identification of human face characteristic point, in turn, obtains the pixel color values of each pixel in human face region, and count
The mean value for calculating all pixels color-values determines other area of skin color of the difference of pixel color values and mean value within a preset range,
The connected region for determining human face region and area of skin color is target area.
In view of if the clothes of user does not obtain noise reduction, to may result in user experience not high, in the present embodiment,
It may further determine that the Garment region connecting with connected region, for example be based on location of pixels and the skills such as pixel color and outline identification
Art, determines Garment region and the connected region is target area.
For example, as shown in figure 4, based on the human face region in face technology identification original image and using face rectangle
Frame (rectangle is inscribed in face) is drawn, and is counted sequentially for face R, G, the B information of face rectangular area, records its average value
For Ra,Ga,Ba, then according to Ra,Ga,Ba, determine area of skin color (in order to comprising colours of skin such as necks), in addition to protection clothes,
The target area of noise reduction can be also done to one piece of region below face.
Step 103, the noise reduction based on artificial intelligence is carried out to the target area of every frame original image and generates noise-reduced image.
It is easily understood that due to the imaging sensor in electronic equipment will receive during shooting it is different degrees of
Photoelectricity magnetic disturbance between peripheral circuit and pixel itself, therefore the obtained original image of shooting inevitably exists and makes an uproar
Sound, also, the difference of annoyance level, the clarity of the image shot be not also identical.Therefore high dynamic range images also must
So there are noise, need further to carry out noise reduction process to high dynamic range images.For example, in night scene photographed scene, usually
It shoots to obtain image using biggish aperture and longer time for exposure, at this time if selecting higher sensitivity to reduce and expose
Between light time, the image shot will necessarily generate noise.And some original images, the interference being subject to when may shoot just compared with
It is small, accordingly, there exist noise it is smaller, if denoising when, only the biggish image of noise is handled, just balances noise reduction
Treating capacity and picture quality.
Specifically, based on noise reduction process is carried out to target original image with the noise-reduction method based on artificial intelligence, it is based on people
The noise reduction of work intelligence is the noise reduction carried out based on noise characteristic, and in the embodiment of the present application, noise characteristic be can be due to image
The statistical property of random noise caused by sensor.Noise said herein mainly includes thermal noise and shot noise, wherein heat is made an uproar
Sound meets Gaussian Profile, and shot noise meets Poisson distribution, and the statistical property in the embodiment of the present application can refer to the variance of noise
Value, naturally it is also possible to be the value of other possible situations, it is not limited here, due to being based on the specific carry out noise reduction of noise, so that not
The same specific corresponding noise of noise obtains different denoisings, and treated, and noise-reduced image can more really retain more
Multidate information can distinguish different noises, and to difference relative to traditional noise-reduction method unified using interpolation method etc.
Noise be adapted to different noise reduction process modes, realize the more lively effect of image after noise reduction.
As a kind of possible implementation, neural network model is trained in advance, which has learnt to obtain
Mapping relations between the sensitivity and noise characteristic of original image, wherein sensitivity is also known as ISO value, refers to measurement egative film
For the index of the sensitivity level of light, ISO value is lower, and the picture quality of acquisition is higher, and image detail shows finer and smoother, ISO value
Higher, light sensing performance is stronger, also more more light can be received, so that more heats are generated, therefore, using opposite
Higher sensitivity would generally introduce more noise, and so as to cause picture quality reduction, i.e. ISO value has close with noise characteristic
Inseparable relationship.
It is appreciated that being trained using the sample image of each sensitivity to neural network model, until neural network mould
When the noise characteristic that type identifies and the noise characteristic marked in respective sample image match, neural network model training is completed.
That is, ambient brightness should be a variety of, under each ambient brightness, multiframe figure is shot in different sensitivity respectively
Picture, as sample image.In order to obtain more accurate noise characteristic recognition result, ambient brightness and ISO can be finely divided,
The frame number of sample image can also be increased, so that after the composograph input neural network model of high dynamic synthesis, the nerve net
Network can accurately identify the statistical property of image.It can be one by above-mentioned neural network model and the Functional Design of noise reduction process
A overall model realizes that the model of the decrease of noise functions can be noise reduction model (the AI Noise based on artificial intelligence
Reduction, AINR).
After the sample image for getting each sensitivity shot under varying environment brightness, using sample image to mind
It is trained through network model.It, will be special by statistics using the statistical property marked in sample image as the characteristic of model training
Property mark sample image input neural network model, to be trained to neural network model, and then identify the system of image
Count characteristic.Certainly, neural network model is only a kind of possible implementation for realizing the noise reduction based on artificial intelligence, in reality
In the implementation procedure of border, the noise reduction based on artificial intelligence can be realized by any other possible mode, for example, can also adopt
It is realized with traditional programming technique (such as simulation and ergonomic method), it for another example, can be with genetic algorithm and artificial mind
Method through network is realized.
Neural network model is trained it should be noted that marking statistical property in sample image, is because
The sample image of mark can clearly represent noise position and the noise type of image, so that the statistical property of mark be made
It can recognize that the statistical property in image after composograph is inputted neural network model for the characteristic of model training.
In turn, based on neural network model is used, noise characteristic identification is carried out to the target area of original image, according to knowledge
Not Chu noise characteristic, noise reduction is carried out to the target area of original image, to obtain noise-reduced image.
Step 104, high dynamic is carried out to all noise-reduced images and is synthetically generated target image.
Specifically, according to all noise-reduced images, synthesis obtains high dynamic range images.In one embodiment of the application
In, the picture format that the display of electronic equipment is capable of handling is yuv format, wherein the luminance signal of image is referred to as Y, color
Degree signal is made of two mutual independent signals, and depending on color system and format difference, two kinds of carrier chrominance signals are frequently referred to as
U and V.In this case, after all noise-reduced images for obtaining RAW format, image-signal processor (Image can be passed through
Signal Processing, ISP) all noise-reduced images are formatted, the noise-reduced image of RAW format is converted to
Yuv format image.Since the display interface size of display is limited, in order to reach better preview effect, can will convert
To yuv format compression of images shown to preview size with carrying out preview.
Certainly, original image light exposure is different, and the image detail of presentation is also different, each image relatively clearly
Region may be different, therefore, in high dynamic synthesis, for the image under night scene screening-mode, if can be improved it is brighter and
The weight in darker region improves the weight in very dark region, then image is enabled to can be realized appropriately in each region
Exposure effect improves image quality.Therefore, it in one embodiment of the application, obtains and inputs height with all noise-reduced images
Dynamic synthetic model obtains the synthetic weight in each region in corresponding original image；Wherein, high dynamic synthetic model has learnt
Mapping relations between the feature and synthetic weight in each region；Feature is used to characterize the image of light exposure and respective image region
Brightness, according to synthetic weight, subregion synthesizes all target noise-reduced images and non-primary image, generates high dynamic range
Enclose image.
It further, can be by using in order to enable the image processing method of the embodiment of the present application has benign cycle
Continue to optimize treatment effect, can also continuous feedback is carried out to neural network model based on the result of image procossing to optimize pair
The parameter answered, as a kind of possible implementation, can also determine in high dynamic range images that is, after obtaining target image
Multiple reference image vegetarian refreshments, the Pixel gray difference between multiple reference image vegetarian refreshments is calculated according to predetermined gradient function, works as pixel
When gray scale difference value is greater than preset threshold, the correspondence parameter of neural network model is modified.
To sum up, the image processing method of the embodiment of the present application, the multiframe original image of photographic subjects object, detection multiframe are former
The corresponding target area of target object in every frame original image, is based on the target area of every frame original image in beginning image
The noise reduction of artificial intelligence generates noise-reduced image, in turn, carries out high dynamic to all noise-reduced images and is synthetically generated target image.By
This, on the one hand, using the noise reduction mode based on artificial intelligence to image noise reduction, remained more while guaranteeing image degree of purity
On the other hand more image details carries out noise reduction process only for target area, substantially increases the efficiency of noise reduction.
Based on above embodiments it should be understood that further highlighting target area to improve the treatment effect of image
Domain so that portrait seems apparent, then can carry out Fuzzy Processing for example, highlighting portrait with nontarget area.
In one embodiment of the application, the noise reduction based on artificial intelligence is carried out to the target area of every frame original image
When, Fuzzy Processing is carried out to the nontarget area of every frame original image and generates noise-reduced image.
Wherein, the degree of Fuzzy Processing can be determined by the distance of camera module and target object, and Fuzzy Processing is more as a result,
Add naturally, i.e. as shown in figure 5, the step of carrying out Fuzzy Processing generation noise-reduced image to the nontarget area of every frame original image can
To include:
Step 201, the distance of the camera module of target object and photographic subjects object is determined.
As a kind of possible example, which can be determined based on the feedback of infrared light, preview graph can also be based on
Accounting as shooting obtained target object substantially estimates the distance.
Step 202, Fuzzy Processing coefficient is determined according to distance.
Wherein, Fuzzy Processing coefficient is the degree that Fuzzy Processing is carried out to the pixel of nontarget area, the Fuzzy Processing
Coefficient is bigger, then Fuzzy Processing degree is bigger.
Step 203, Fuzzy Processing is carried out according to nontarget area of the Fuzzy Processing coefficient to every frame original image and generates drop
It makes an uproar image.
Specifically, the mode of Fuzzy Processing can be Fill Color or pattern etc., it is also possible to cover what user liked
Other patterns, the color of filling or the transparency of pattern can indicate the degree of the Fuzzy Processing.
To sum up, the image processing method of the embodiment of the present application carries out noise reduction process only for target area, for non-mesh
It marks region and carries out Fuzzy Processing, while ensure that noise reduction speed, highlighted the target subject of shooting, meet of user
Property demand.
In order to realize above-described embodiment, the application also proposes a kind of image processing apparatus.
Fig. 6 is according to the structural schematic diagram of the image processing apparatus of the application one embodiment, as shown in fig. 6, the device
It include: shooting module 10, detection module 20, noise reduction module 30 and synthesis module 40, wherein
Shooting module 10, the multiframe original image for photographic subjects object.
Detection module 20, for detecting in multiframe original image the corresponding target area of target object in every frame original image
Domain.
Noise reduction module 30 carries out the noise reduction based on artificial intelligence for the target area to every frame original image and generates noise reduction
Image.
Synthesis module 40 is synthetically generated target image for carrying out high dynamic to all noise-reduced images.
In one embodiment of the application, synthesis module 40, specifically for the target area to every frame original image into
Noise reduction of the row based on artificial intelligence, and Fuzzy Processing is carried out to the nontarget area of every frame original image and generates noise-reduced image.
It should be noted that the aforementioned explanation to image processing method, is also applied for the image procossing of the embodiment of the present application
Device, realization principle is similar, and details are not described herein.
To sum up, the image processing apparatus of the embodiment of the present application, the multiframe original image of photographic subjects object, detection multiframe are former
The corresponding target area of target object in every frame original image, is based on the target area of every frame original image in beginning image
The noise reduction of artificial intelligence generates noise-reduced image, in turn, carries out high dynamic to all noise-reduced images and is synthetically generated target image.By
This, on the one hand, using the noise reduction mode based on artificial intelligence to image noise reduction, remained more while guaranteeing image degree of purity
On the other hand more image details carries out noise reduction process only for target area, substantially increases the efficiency of noise reduction.
In order to realize above-described embodiment, the application also proposes a kind of electronic equipment 200, referring to Fig. 7, comprising: image sensing
Device 210, processor 220, memory 230 and it is stored in the computer journey that can be run on memory 230 and on processor 220
Sequence, described image sensor 210 are electrically connected with the processor 220, when the processor 220 executes described program, are realized such as
Image processing method described in above-described embodiment.
In order to realize above-described embodiment, the embodiment of the present application also provides a kind of storage mediums, when in the storage medium
Instruction when being executed by processor so that the processor executes following steps: the multiframe original image of photographic subjects object；
Detect the corresponding target area of target object described in every frame original image in the multiframe original image；
Noise reduction based on artificial intelligence is carried out to the target area of every frame original image and generates noise-reduced image；
High dynamic is carried out to all noise-reduced images and is synthetically generated target image.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is contained at least one embodiment or example of the application.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the present application, the meaning of " plurality " is at least two, such as two, three
It is a etc., unless otherwise specifically defined.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the application includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be by the application
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or pass
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wirings
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can then be edited, be interpreted or when necessary with it for example by carrying out optical scanner to paper or other media
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the application can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.Such as, if realized with hardware in another embodiment, following skill well known in the art can be used
Any one of art or their combination are realized: have for data-signal is realized the logic gates of logic function from
Logic circuit is dissipated, the specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), scene can compile
Journey gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, can integrate in a processing module in each functional unit in each embodiment of the application
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..Although having been shown and retouching above
Embodiments herein is stated, it is to be understood that above-described embodiment is exemplary, and should not be understood as the limit to the application
System, those skilled in the art can be changed above-described embodiment, modify, replace and become within the scope of application
Type.
Claims (12)
1. a kind of image processing method, which comprises the following steps:
The multiframe original image of photographic subjects object；
Detect the corresponding target area of target object described in every frame original image in the multiframe original image；
Noise reduction based on artificial intelligence is carried out to the target area of every frame original image and generates noise-reduced image；
High dynamic is carried out to all noise-reduced images and is synthetically generated target image.
2. the method as described in claim 1, which is characterized in that the target object is face, and the detection multiframe is former
The corresponding target area of target object described in every frame original image in beginning image, comprising:
Detect the human face region of every frame original image；
The pixel color values of each pixel in the human face region are obtained, and calculate the mean value of all pixels color-values；
Determine other area of skin color of the difference of pixel color values and the mean value within a preset range；
The connected region for determining the human face region and the area of skin color is the target area.
3. method according to claim 2, which is characterized in that determine the connected region of the human face region and the area of skin color
Domain is after the target area, further includes:
Determine the Garment region connecting with the connected region；
The target area is corrected according to the Garment region.
4. the method as described in claim 1, which is characterized in that the target area to every frame original image carries out base
Noise-reduced image is generated in the noise reduction of artificial intelligence, comprising:
Noise reduction based on artificial intelligence is carried out to the target area of every frame original image, and to every frame original image
The non-target area carries out Fuzzy Processing and generates the noise-reduced image.
5. method as claimed in claim 4, which is characterized in that the non-target area described and to every frame original image
Domain carries out Fuzzy Processing and generates the noise-reduced image, comprising:
It determines the target object and shoots the distance of the camera module of the target object；
Fuzzy Processing coefficient is determined according to the distance；
Fuzzy Processing, which is carried out, according to the non-target area of the Fuzzy Processing coefficient to every frame original image generates institute
State noise-reduced image.
6. the method as described in claim 1, which is characterized in that the target area to every frame original image carries out base
Noise-reduced image is generated in the noise reduction of artificial intelligence, comprising:
Using neural network model, noise characteristic identification is carried out to the target area of the original image；Wherein, the nerve net
Network model has learnt to obtain the mapping relations between the sensitivity and noise characteristic of the original image；
According to the noise characteristic identified, noise reduction is carried out to the target area, to obtain the noise-reduced image.
7. method as claimed in claim 6, which is characterized in that the neural network model is the sample using each sensitivity
Image is trained the neural network model, until noise characteristic and respective sample that the neural network model identifies
When the noise characteristic matching marked in image, the neural network model training is completed.
8. the method as described in claim 1, which is characterized in that the multiframe original image of the photographic subjects object, comprising:
Benchmark light exposure is determined according to the ambient brightness of photographed scene；
At least first original image of frame is shot according to the benchmark light exposure；
Shooting is lower than at least second original image of frame for the benchmark light exposure；
Shooting is higher than an at least frame third original image for the benchmark light exposure.
9. a kind of image processing apparatus characterized by comprising
Shooting module, the multiframe original image for photographic subjects object；
Detection module, for detecting the corresponding target area of target object described in every frame original image in the multiframe original image
Domain；
Noise reduction module carries out the noise reduction based on artificial intelligence for the target area to every frame original image and generates noise reduction figure
Picture；
Synthesis module is synthetically generated target image for carrying out high dynamic to all noise-reduced images.
10. device as claimed in claim 9, which is characterized in that
The synthesis module carries out the noise reduction based on artificial intelligence specifically for the target area to every frame original image,
And Fuzzy Processing is carried out to the non-target area of every frame original image and generates the noise-reduced image.
11. a kind of electronic equipment characterized by comprising imaging sensor, memory, processor and storage are on a memory
And the computer program that can be run on a processor, described image sensor are electrically connected with the processor, the processor is held
When row described program, such as image processing method described in any one of claims 1-8 is realized.
12. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
Such as image processing method described in any one of claims 1-8 is realized when being executed by processor.
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