CN106504279A - Coloured image auto focusing method - Google Patents

Abstract

The invention discloses a kind of coloured image auto focusing method, methods described include coloured image antagonism figure obtain, focusing on sampled point collection and improve the sharpness evaluation function of Brenner based on focus window.The method of the invention, make full use of the colouring information of coloured image first, it is to avoid the loss of caused color detail when only going to evaluate definition using gray-scale maps；Secondly, consider the importance of characteristic and golden section point that field of view center is located at subject goal more, reduce the negative effect of the amount of calculation and nontarget area increased because of statistical color information, be effectively improved real-time and the accuracy of focusing；Finally, by improving Brenner functions, on the premise of amount of calculation is not increased, more comprehensively reflect the definition of entire image.

Description

Coloured image auto focusing method

Technical field

The invention belongs to technical field of image processing, more particularly, to a kind of coloured image auto focusing method.

Background technology

With automation equipment, intelligentized development, become photographing unit, video camera, shown based on the automatic focusing of image The key technology of the imaging systems such as micro mirror.Realize based on the key of the Techniques of Automatic Focusing of image it being that construction one is quick, reliable Sharpness evaluation function, the position corresponding to the Function Extreme Value be imaging focal position.Due to image definition evaluation Function need to imaging system gradually convergence focal position to just arriving this complete mistake away from focal position again positioned at focal position Great amount of images in journey all carries out the evaluation of defocusing amount, therefore, a good image definition function except possess unbiasedness, Outside unimodality, sharp and noise immunity, to also be easy to as far as possible calculate, to meet the need of automatic focusing real-time Will.

In order to reduce amount of calculation, existing sharpness evaluation function is calculated just for gray-scale maps, causes a large amount of face The loss of color information.However, in a large amount of actual scene images such as biomedicine, the colouring information of target is most important, loses Colouring information may lead to not obtain best focus position；And comment if all calculating definition to tri- Color Channel figures of RGB Valency function, is difficult to avoid that the problem that amount of calculation is greatly increased again.

Content of the invention

Disadvantages described above or Improvement requirement for prior art, the invention provides a kind of coloured image side of focusing automatically Method, its object is to both make full use of image color information, can realize fast automatic focusing again.Technical side proposed by the present invention Case is as follows：

A kind of coloured image auto focusing method, it is characterised in that methods described includes following step：

(1) original image of rgb format is input into, red-green antagonism figure I is calculated respectively_rgWith blue-yellow antagonism figure I_by；

(2) obtain and focus on sampled point；

Original image is divided into three class focus windows first：Center window, four side window mouths and corner window；

To the red-green antagonism figure I in different focus windows_rgWith blue-yellow antagonism figure I_bySet different sampling policies： In the window of center, pixel carries out fully sampled, and in four side window mouths, pixel carries out horizontal dot interlace sampling, in the window of corner pixel laterally and Longitudinal direction carries out dot interlace sampling respectively；Respectively obtain after sampling and red-green antagonism figure I_rgCorresponding sampled point chained list first L_rg, and blue- Yellow antagonism figure I_byCorresponding sampled point chained list second L_by, described sampled point chained list first L_rg, sampled point chained list second L_byIn have recorded poly- The picture element position information of burnt sampled point；

(3) red-green antagonism figure I is calculated respectively using improved Brenner functions_rgFocus on the functional value of sampled point with blue- Yellow antagonism figure I_byFocus on the functional value F of sampled point_MBrenner, all functional value sums of the statistics more than given threshold value T, by this Statistical value evaluates the definition of coloured image.

The concrete calculating process of described step (1) is as follows：

I_rg=R-G

I_by=B-Y

Wherein,

Y=(R+G)/2

R, G, B are respectively three Color Channels of red, green, blue of input color image.

In step (2)：4 adjacent subgraphs of original image central point constitute center window W_a；Original image corner 4 subgraphs constitute corner window W_c；In original image, corner window W_c, center window W_aPart in addition constitutes four side window mouths W_b.

Improvement Brenner function computing formula in step (3) are as follows：

Wherein, L_c.length it is sampled point chained list L_cLength,

MB_c(i)=| I_c(L_c[i] .x+2, L_c[i].y)-I_c(L_c[i] .x, L_c[i].y)|×

|I_c(L_c[i] .x, L_c[i].y+2)-I_c(L_c[i] .x, L_c[i] .y) | c=rg, by

Wherein, (L_c[i] .x, L_c[i] .y) it is sampled point chained list L_cIn i-th element location of pixels.

What the present invention can reach has the beneficial effect that：

Very high due to focusing on automatically the requirement to real-time, in the design of substantial amounts of sharpness evaluation function, in order to subtract Few amount of calculation and lose colouring information so that under some specific occasions cannot exact evaluation coloured image definition.And this The method of bright proposition allows the color characteristic of image to maximize the use on the premise of amount of calculation is not increased, to coloured image The evaluation result of definition is more accurate.

Description of the drawings

Fig. 1 is the flow chart of the coloured image auto focusing method of the present invention；

Fig. 2 is the division schematic diagram of focus window in the present invention.

Specific embodiment

In order that the objects, technical solutions and advantages of the present invention become more apparent, below in conjunction with drawings and Examples, right The present invention is further elaborated.

Step one, calculates red-green antagonism figure, blue-yellow antagonism figure：

(1.1) original image of RGB patterns is input into, and its width is calculated as W, and which is highly designated as H；Original image is decomposed into red (R), green (G) and blue (B) three color channel images；

(1.2) yellow (Y) channel image is obtained using red, green two color channel images；

Y=(R+G)/2

(1.3) red-green antagonism figure I is calculated using R, G, B, Y_rgWith blue-yellow antagonism figure I_by；

I_rg=R-G

I_by=B-Y

Step 2, samples to the pixel position of original image formed objects, builds sampled point chained list first L respectively_rg With sampled point chained list second L_by.Sampled point chained list first L_rgFollow-up red-green antagonism figure I will be used for_rgDefinition calculating, sampled point Chained list second L_byBlue-yellow antagonism figure I will be used for_byDefinition calculating.The purpose for building sampled point chained list is have by extraction The representational amount of calculation for focusing on the follow-up sharpness evaluation function of sampled point minimizing.Red-green antagonism figure I_rgWith blue-yellow antagonism figure I_byThe respective selection principle for focusing on sampled point is the locations complementary for making two sampled point chained lists as far as possible, is reducing amount of calculation Simultaneously as far as possible fully using the colouring information of entire image.Concrete steps include：

(2.1) original image of input is divided into 16 subgraphs by size, each subgraph size is W/4 × H/4；

(2.2) as shown in Fig. 2 16 subgraphs are divided three classes focus window：Center window W_a, four side window mouth W_bAnd Corner window W_c；

4 adjacent subgraphs of original image central point constitute center window W_a；

4 subgraphs of original image corner constitute corner window W_c；

In original image, corner window W_c, center window W_aPart in addition constitutes four side window mouth W_b.

(2.3) records center window W_aThe position (x, y) of each pixel interior, adds sampled point chained list first L_rgIn；In record Heart window W_aThe position of each pixel interior, adds sampled point chained list second L_byIn；

(2.4) to four side window mouth W_bInterior pixel carries out horizontal dot interlace sampling, specifically, records four side window mouth W line by line_b The position (x, y) of interior odd bits pixel, and it is added into sampled point chained list first L_rgIn；Four side window mouth Ws are recorded line by line_bInterior The position (x, y) of even bit pixel, and it is added into sampled point chained list second L_byIn；

(2.5) to corner window W_cInterior pixel carries out 2 times of down-samplings, i.e., horizontal and vertical carry out dot interlace sampling respectively, And sampled pixel position is added sampled point chained list first L_rgOr sampled point chained list second L_byIn.

Specifically, by corner window W_cSome subwindows, the left side in the subwindow are divided into respectively according to 2 × 2 pixels The position (x, y) of upper angle pixel adds sampled point chained list first L_rgIn, the position (x, y) of the lower right corner pixel in the subwindow adds Enter sampled point chained list second L_byIn.

Due to being located at field of view center subject goal more, therefore center window W_aThe sample rate highest of interior pixel；Meanwhile, taking the photograph In shadow composition, the effect of golden section point is most important, therefore, center window W_aSize arrange cause golden section point lucky It is located therein.Relative to center window W_a, four side window mouth W_bWith corner window W_cInterior nontarget area is increased, therefore, this two class The sample rate of window is relatively low.It is 9/8 × W that the setting of focus window and sampling policy causes the length sum of two sampled point chained lists × H, slightly larger than original image size.

Step 3, using all sharpness evaluation function values for focusing on sampled point (x, y) of improved Brenner functions statistics Summation F_MBrenner：

(3.1) red-green antagonism figure I is counted using improved Brenner function formulas_rgBelong to sampled point chained list first in figure L_rgIn each pixel functional value and blue-yellow antagonism figure I_byInside belong to sampled point chained list second L_byIn each pixel function Value：

MB_c(i)=| I_c(L_c[i] .x+2, L_c[i].y)-I_c(L_c[i] .x, L_c[i].y)|×

|I_c(L_c[i] .x, L_c[i].y+2)-I_c(L_c[i] .x, L_c[i] .y) | c=rg, by

Wherein, (L_c[i] .x, L_c[i] .y) it is sampled point chained list L_cIn i-th element location of pixels.

Improved Brenner functions not only allow for the gray scale difference that horizontal direction differs the pixel of two units, it is also contemplated that Vertical direction differs the gray scale difference of the pixel of two units, can more comprehensively reflect the partial gradient change of entire image, meanwhile, Original Brenner functions excessively do not increase amount of calculation relatively.

(3.2) all functional value F more than given threshold value T are counted_MBrennerSum, as input color image clear Degree.

Wherein, L_c.length it is sampled point chained list L_cLength.

The corresponding program of this method can in embedded images collecting device, in operation process, by contrast result of calculation several times, Select highest statistical value, you can real-time selection best focus scheme.

Coloured image auto focusing method provided by the present invention, makes full use of the colouring information of coloured image first, keeps away The loss of caused color detail when having exempted from only to go to evaluate definition using gray-scale maps；Secondly, consider subject goal multidigit In the importance of the characteristic and golden section point of field of view center, reduce the amount of calculation that increases because of statistical color information and The negative effect of nontarget area, is effectively improved real-time and the accuracy of focusing；Finally, by improving Brenner letters Number, on the premise of amount of calculation is not increased, more comprehensively reflects the definition of entire image.

Claims (4)

1. a kind of coloured image auto focusing method, it is characterised in that methods described includes following step：

(1) original image of rgb format is input into, red-green antagonism figure I is calculated respectively_rgWith blue-yellow antagonism figure I_by；

(2) obtain and focus on sampled point；

Original image is divided into three class focus windows first：Center window, four side window mouths and corner window；

To the red-green antagonism figure I in different focus windows_rgWith blue-yellow antagonism figure I_bySet different sampling policies：Central window In mouthful, pixel carries out fully sampled, and in four side window mouths, pixel carries out horizontal dot interlace sampling, horizontal and vertical point of pixel in the window of corner Dot interlace sampling is not carried out；Respectively obtain after sampling and red-green antagonism figure I_rgCorresponding sampled point chained list first L_rg, and blue-yellow antagonism Figure I_byCorresponding sampled point chained list second L_by, described sampled point chained list first L_rg, sampled point chained list second L_byIn have recorded focusing sampling The picture element position information of point；

(3) red-green antagonism figure I is calculated respectively using improved Brenner functions_rgThe functional value for focusing on sampled point is short of money with blue-yellow Anti- figure I_byFocus on the functional value F of sampled point_MBrenner, all functional value sums of the statistics more than given threshold value T, by the statistics Value evaluates the definition of coloured image.

2. coloured image definition evaluation methodology according to claim 1, it is characterised in that described step (1) is concrete Calculating process is as follows：

I_rg=R-G

I_by=B-Y

Wherein,

Y=(R+G)/2

R, G, B are respectively three Color Channels of red, green, blue of input color image.

3. coloured image definition evaluation methodology according to claim 1 and 2, it is characterised in that：In step (2), 4 adjacent subgraphs of original image central point constitute center window W_a；4 subgraphs of original image corner constitute corner window W_c； In original image, corner window W_c, center window W_aPart in addition constitutes four side window mouth W_b.

4. coloured image definition evaluation methodology according to claim 1 and 2, it is characterised in that in step (3) Improve Brenner function computing formula as follows：

$F_{MBrenner}=\underset{c=rg,by}{\mathrm{\Σ}}\underset{i=1}{\overset{L_c.length}{\mathrm{\Σ}}}\[{\mathrm{MB}}_c\left(i\right)>T\]$

Wherein, L_c.length it is sampled point chained list L_cLength,

MB_c(i)=| I_c(L_c[i] .x+2, L_c[i].y)-I_c(L_c[i] .x, L_c[i].y)|×|I_c(L_c[i] .x, L_c[i].y+2)- I_c(L_c[i] .x, L_c[i] .y) | c=rg, by

Wherein, (L_c[i] .x, L_c[i] .y) it is sampled point chained list L_cIn i-th element location of pixels.