CN107592470A - A kind of exposure algorithm applied to more lens sensors - Google Patents
A kind of exposure algorithm applied to more lens sensors Download PDFInfo
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Abstract
The invention belongs to image processing field, and in particular to a kind of exposure algorithm applied to more lens sensors, comprise the following steps：Step 1: after reading the image of more lens sensors shootings, the image entropy C and current exposure time T of described image are calculated；Step 2: the image entropy C of the image obtained in step 1 is compared with setting numerical value A, if image entropy C is more than or equal to setting numerical value A, described image exposure is normal；Otherwise step 3 is jumped to；Step 3: calculate the gray average V of described image, the gray average V is compared with setting numerical value B, and more lens sensors are delivered into the instruction for turning or tuning up time for exposure T down, and step 1 is jumped to so that more lens sensors re-shoot new image；The algorithm can be such that image integral brightness level is maintained in a proper range.
Description
Technical field
The invention belongs to image processing field, and in particular to a kind of exposure algorithm applied to more lens sensors.
Background technology
Because the factor of external environment, functions of the equipments etc. is to gained picture quality in the gatherer process of image information
Deleterious effect is caused, even can lose bulk information sometimes.Therefore, digital picture pretreatment comes as a kind of technology of early stage
Picture quality early stage is ensured and lifted, quality information source is provided for follow-up work.Therefore, the performance of digital picture Preprocessing Algorithm
There is conclusive influence for the quality of final image.
Generally, the various automatic exposure algorithms proposed at present are all that certain work is played to specific application environment
With.However, it is difficult to have an omnipotent automatic exposure camera accurate to it for present Protean boundless universe environment
True exposure.So some parameters in automatic exposure algorithm can be proposed optionally special by the environment to be worked according to camera
Fixed requirement, finally being capable of accurate automatic exposure to observed environment.
Existing exposure algorithm：Objectively image brightness distribution can be described for histogram, therefore some automatic exposures
Light algorithm, accurate automatic exposure is carried out according to the distribution of shooting image histogram.By analyzing image histogram, it can be deduced that
Captured image is overexposure or under-exposure, by adjusting exposure parameter and adjusting reference brightness according to histogram
Compensation, finally makes exposure more accurate.By adjusting exposure parameter, the image histogram both ends for making to finally obtain slowly level off to
Zero.Image information after record exposure that so can be maximum, resulting image pixel is avoided to occur " dead black " or " dead
Point in vain ".
Algorithm based on brightness histogram thinks that peak region represents the uninterested region of human eye in histogram, by for
Peak allocation different weights value calculates brightness of image in histogram, and its implementation process is complex.
Mean flow rate algorithm adjusts lower exposed frame by calculating current frame image mean flow rate and combining sensor characteristics
Time.
Weight equal value algorithm is different from mean flow rate algorithm, by distributing different weights value for different zones to calculate image
Brightness.By increasing the weighted value of target area, image weighted luminance average is calculated, it is calculated the average brightness of gained more
The information of target area can be highlighted.Therefore in the case of backlight or frontlighting, the picture quality shot is all fine.
Algorithm based on image entropy thinks image information entropy and time for exposure parabolically relation, and when image information entropy reaches
During to maximum, optimum exposure image is obtained, and proposes a kind of automatic exposure algorithm based on climbing method accordingly.This algorithm iteration
The clear and definite standard of process neither one, it may result in iteration Infinite Cyclic and go down.
The content of the invention
Present invention aim to address being played a role present in prior art to specific application environment, apply
Scope is too small.
Therefore, the invention provides a kind of algorithm applied to more lens sensors, be mainly used in equipment of taking photo by plane it is quick,
Accurately exposure.The algorithm can be such that image integral brightness level is maintained in a proper range.
The technical problem to be solved in the present invention is achieved through the following technical solutions：
A kind of exposure algorithm applied to more lens sensors, comprise the following steps：
Step 1: after reading the image of more lens sensors shootings, the image entropy C and current exposure of described image are calculated
Time T；
Step 2: the image entropy C of the image obtained in step 1 is compared with setting numerical value A, if image entropy C is big
In equal to setting numerical value A, then described image exposure is normal；Otherwise step 3 is jumped to；
Step 3: the gray average V, the gray average V that calculate described image are compared with setting numerical value B, if should
Gray average V is more than setting numerical value B, then described image is over-exposed, and the instruction for turning time for exposure T down is delivered into more camera lenses senses
Device, and step 1 is jumped to so that more lens sensors re-shoot new image；
If gray average V is less than setting numerical value B, described image is under-exposed, and the instruction for tuning up time for exposure T is sent
At most lens sensors, and step 1 is jumped to so that more lens sensors re-shoot new image；
The setting numerical value A is less than the setting numerical value B.
In a preferred embodiment of the invention, the computational methods of the gray average V：Image all pixels R, G, B
Summation and then divided by total pixel number again divided by port number 3.
In a preferred embodiment of the invention, the numerical value A that sets is 7-9.
In a preferred embodiment of the invention, the numerical value B that sets is 120-140, and preferably 128.
In a preferred embodiment of the invention, the instruction T1=T-delta for turning time for exposure T down, it is described
Delta is step factor.
In a preferred embodiment of the invention, the instruction T2=T+delta for tuning up time for exposure T, it is described
Delta is step factor.
In a preferred embodiment of the invention, more lens sensors are applied in the step 3 and tunes up or adjust
In small time for exposure T instruction process, if extreme point occurs in the image entropy C of image, regulation next time step factor is reduced into original
/ 10th come, i.e. delta/10.
In a preferred embodiment of the invention, described image entropy C extreme point is 7.1 or 8.
Beneficial effects of the present invention：
This algorithmic procedure is simple, has good exposure effect, meets the requirement of multicamera system exposure.
The present invention is described in further details below with reference to drawings and Examples.
Brief description of the drawings
Fig. 1 is time for exposure and the diagram of comentropy of the present invention.
Fig. 2 is time for exposure and the partial enlarged drawing of comentropy of the present invention.
Fig. 3 is Fig. 2 schematic diagram.
Fig. 4 is the workflow diagram of the present invention.
Embodiment
For the technological means and effect that the present invention reaches predetermined purpose and taken is expanded on further, below in conjunction with accompanying drawing and reality
Example embodiment, architectural feature and its effect to the present invention are applied, is described in detail as follows.
This paper algorithm is on the basis of based on image entropy automatic exposure algorithm, the method that adds curve matching, very greatly
The efficiency of original algorithm is improved in degree.
Comentropy：
This concept of comentropy is proposed by Shannon, be defined as it is discrete be chance event occur probability.Calculation formula
It is as follows：
Wherein, what x was represented is stochastic variable, and p (x) represents output probability function.When variable x uncertainty is bigger,
Entropy is also bigger.
Comentropy is introduced into image procossing, derives picture entropy, for a secondary picture, in its picture of different positions
Different gray values is known as, the gray value of pixel is represented with Xi, I=1,2,3 ... n；N represents the series of gradation of image；Pi is each
The probability that individual number of greyscale levels occurs in the picture, according to the calculation formula of comentropy, show that image entropy is：
When image exposure value changes, image entropy is also with change, when image exposure reaches it is optimal when Image entropy will reach
To maximum, for a sub-picture, if its intensity profile is than more uniform, then calculated in this case by (2)
Image entropy is maximum.That is, when the probability of all gray levels of picture is identical, i.e.,
Be aware of when the probability of occurrence of all gray levels is identical be camera exposure levels it is optimal, but this is substantially one
The preferable state of kind, a sub-picture are difficult make it that all gray level probability are equal.
According to principles above, the core of this algorithm is when controlling the exposure of camera in turn in order to find maximum image entropy
Between, so as to reach optimum exposure.
Fig. 1 is the graph of a relation of camera exposure time and image information entropy, and Fig. 2 is the amplification to Fig. 1 extensive parts, can be seen
To the time for exposure of camera and image entropy similar to parabolic relation (such as figure three), the peak point of image is looked for, turns into algorithm research
Key.
The algorithm studied herein is mainly used in the camera system for possessing multiple camera lenses, and each camera lens, which needs to reach, takes pictures together
Step, therefore, quick, the suitable time for exposure is the emphasis studied herein.The above-mentioned exposure algorithm based on comentropy, iterations
Excessively, termination factor is chosen improper, causes efficiency comparison low, is not suitable for the system.
Under conditions of 256 gray levels, the maximum informational entropy C that is calculatedmax=log (256)=8, is obtained by the formula
Data 8 ideally show that the picture gray level shot under normal scene will not be equally distributed.
Shown by many experiments when the comentropy of image is more than 7.1, the exposure effect of image closest to optimum exposure,
So entropy 7.1 can be used as a controlling elements, can directly it be taken pictures when entropy is more than 7.1, when entropy is less than 7.1
When, taken pictures after the exposure of flow shown in figure below can be used：
Specifically：
A kind of exposure algorithm applied to more lens sensors, comprise the following steps：
Step 1: after reading the image of more lens sensors shootings, the image entropy C and current exposure of described image are calculated
Time T；
Step 2: the image entropy C of the image obtained in step 1 is compared with setting numerical value A, if image entropy C is big
In equal to setting numerical value A, then described image exposure is normal；Otherwise step 3 is jumped to；
Step 3: the gray average V, the gray average V that calculate described image are compared with setting numerical value B, if should
Gray average V is more than setting numerical value B, then described image is over-exposed, and the instruction for turning time for exposure T down is delivered into more camera lenses senses
Device, and step 1 is jumped to so that more lens sensors re-shoot new image；
If gray average V is less than setting numerical value B, described image is under-exposed, and the instruction for tuning up time for exposure T is sent
At most lens sensors, and step 1 is jumped to so that more lens sensors re-shoot new image；
The setting numerical value A is less than the setting numerical value B, wherein setting numerical value A as 7-9, sets numerical value
B is 120-140, preferably 128.
Above-mentioned gray average V computational methods：Image all pixels R, G, B summation are then divided by total pixel number is again divided by logical
Road number 3.
And turning time for exposure T instruction T1=T-delta down, the delta is step factor；Tune up time for exposure T's
T2=T+delta is instructed, the delta is step factor.Step factor is without the value determined, the difference of its foundation time for exposure
And change, for example when the time for exposure is too small, a slightly bigger step factor can be added.
Further, in step 3 more lens sensors are applied with the instruction process for tuning up or turning down time for exposure T
In, if extreme point (7.1 or 8) occurs in the image entropy C of image, regulation next time step factor is reduced into original 1/10th,
That is delta/10.
This algorithmic procedure is simple, has good exposure effect, meets the requirement of multicamera system exposure.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to is assert
The specific implementation of the present invention is confined to these explanations.For general technical staff of the technical field of the invention,
On the premise of not departing from present inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the present invention's
Protection domain.
Claims (8)
1. a kind of exposure algorithm applied to more lens sensors, it is characterised in that comprise the following steps：
Step 1: after reading the image of more lens sensors shootings, the image entropy C and current exposure time of described image are calculated
T；
Step 2: by the image entropy C of the image obtained in step 1 with setting numerical value A be compared, if image entropy C be more than etc.
In setting numerical value A, then described image exposure is normal；Otherwise step 3 is jumped to；
Step 3: the gray average V, the gray average V that calculate described image are compared with setting numerical value B, if the gray scale
Average V is more than setting numerical value B, then described image is over-exposed, and the instruction for turning time for exposure T down is delivered into more lens sensors,
And step 1 is jumped to so that more lens sensors re-shoot new image；
If gray average V is less than setting numerical value B, described image is under-exposed, the instruction for tuning up time for exposure T is delivered to more
Lens sensors, and step 1 is jumped to so that more lens sensors re-shoot new image；
The setting numerical value A is less than the setting numerical value B.
2. a kind of exposure algorithm applied to more lens sensors as claimed in claim 1, it is characterised in that the gray scale is equal
Value V computational methods：Image all pixels R, G, B summation and then divided by total pixel number again divided by port number 3.
A kind of 3. exposure algorithm applied to more lens sensors as claimed in claim 1, it is characterised in that the setting number
Value A is 7-9.
A kind of 4. exposure algorithm applied to more lens sensors as claimed in claim 1, it is characterised in that the setting number
Value B is 120-140, preferably 128.
5. a kind of exposure algorithm applied to more lens sensors as described in one of claim 1-4, it is characterised in that described
The instruction T1=T-delta, the delta for turning time for exposure T down are step factor.
6. a kind of exposure algorithm applied to more lens sensors as described in one of claim 1-4, it is characterised in that described
The instruction T2=T+delta, the delta for tuning up time for exposure T are step factor.
A kind of 7. exposure algorithm applied to more lens sensors as described in claim 5 or 6, it is characterised in that the step
More lens sensors are applied in the instruction process for tuning up or turning down time for exposure T in rapid three, if the image entropy C of image goes out
Existing extreme point, then adjust step factor and be reduced into original 1/10th, i.e. delta/10 next time.
A kind of 8. exposure algorithm applied to more lens sensors as claimed in claim 7, it is characterised in that described image entropy
C extreme point is 7.1 or 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112887636A (en) * | 2021-01-28 | 2021-06-01 | 北京华捷艾米科技有限公司 | Infrared image exposure method, device, equipment and storage medium |
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| CN103702015A (en) * | 2013-12-20 | 2014-04-02 | 华南理工大学 | Exposure control method for human face image acquisition system under near-infrared condition |
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Patent Citations (6)
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| JP2008085634A (en) * | 2006-09-27 | 2008-04-10 | Konica Minolta Holdings Inc | Imaging apparatus and image processing method |
| JP2008219230A (en) * | 2007-03-01 | 2008-09-18 | Konica Minolta Holdings Inc | Imaging apparatus, and image processing method |
| CN103237175A (en) * | 2013-04-17 | 2013-08-07 | 中国科学院西安光学精密机械研究所 | Digital camera automatic exposure method based on human visual characteristics |
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| CN103702015A (en) * | 2013-12-20 | 2014-04-02 | 华南理工大学 | Exposure control method for human face image acquisition system under near-infrared condition |
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Address after: Room 504, Block E, HUanpu science and Technology Industrial Park, 211 tianguba Road, high tech Zone, Xi'an City, Shaanxi Province, 710000 Applicant after: Tudou Data Technology Group Co.,Ltd. Address before: Room 504, Block E, HUanpu science and Technology Industrial Park, 211 tianguba Road, high tech Zone, Xi'an City, Shaanxi Province, 710075 Applicant before: SHAANXI TUDOU DATA TECHNOLOGY Co.,Ltd. |
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Application publication date: 20180116 |