Accurate auto-focus method

1. Technical Field

The present disclosure relates to auto-focus technologies, and particularly to an auto-focus method capable of accurately finding a focused position.

2. Description of Related Art

Auto-focus methods are used in a camera to find a focused position. An auto-focus method utilizes a mountain climbing algorithm, in which a focusing lens of the camera is driven to make a step by step global search. The image resolution of the lens at each step is acquired to plot a curve of the image resolution relative to the step of the focusing lens, and then the step associated with the highest image resolution is taken as the focused position. However, if there is a slightly shake occurring during the focusing, the curve may distort, disabling the mountain climbing algorithm to find the real highest image contrast and resulting in an inaccurate focusing result.

Therefore, it is desirable to provide an auto-focus method, which can overcome the limitations described.

FIG. 1 is a cross-sectional schematic view of a lens module in accordance with an exemplary embodiment.

FIG. 2 is a flowchart of an auto-focus method in accordance with an exemplary embodiment.

FIG. 3 is a flowchart of calculating an optimal focused position of the auto-focus method of FIG. 1 .

FIG. 4 is a coordinate graph of a resolution value and a current value of the auto-focus method of FIG. 1 .

Embodiments of the disclosure will be described with reference to the drawings.

Referring to FIG. 1 , an auto-focus method, according to an exemplary embodiment, is used in an electronic device, such as camera and telephone, including a lens module 100 . The lens module 100 includes a focusing lens 10 and a motor 20 for driving the focusing lens 10 to move within a focus range of the lens module 100 . A resolution (i.e. contrast) of an image captured by the lens module 100 is changed with a position of the focusing lens 10 .

Referring to FIGS. 2-4 , the auto-focus method may be implemented by software modules encoded via a program language, such as C++ or hardware constructed to have the corresponding function. In the former case, the software modules can be stored in a memory and run by a processor. The auto-focus method includes steps S 11 -S 18 .

In step S 11 : searching boundaries InitL, InitR of a searching range of the focusing lens 10 are represented by L, R. The resolutions of images captured by the lens module 100 are represented by f(L), f(R) when the focusing lens 10 positioned on the two searching boundaries InitL, InitR respectively. Wherein L and R are variables, InitL and InitR are constants. f(X) is the resolution of the image captured by the lens module 100 when the focusing lens 10 positioned on a focused position X, wherein X is a variable greater than InitL and lower than InitR. The focused position X is a current value used for driving the motor 20 . The searching boundaries InitL, InitR are determined according to the designed requirements of the lens module 100 . In this embodiment, L=InitL=0.2A, R=InitR=2A, f(L)=0.07, and f(R)=0.07.

In step S 12 : two focused positions X 1 , X 2 are defined by a golden section method, wherein X 1 =L+(1−τ)×(R−L), X 2 =L+τ×(R−L), and τ=0.618. f(X 1 ) and f(X 2 ) are the resolution of the images captured by the lens module 100 when the focusing lens 10 positioned on the focused positions X 1 , X 2 . Wherein X 1 and X 2 are variables. In the embodiment, X 1 =0.8696, X 2 =1.3124, f(X 1 )=0.19, f(X 2 )=0.15.

In step S 13 : f(X 1 ) is compared with f(X 2 ) to determine whether or not f(X 1 ) is greater than f(X 2 ) when f(X 1 ) is lower than f(X 2 ), entering into step S 14 . When f(X 1 ) is greater than f(X 2 ), entering into step S 15 . In the embodiment, f(X 1 ) is greater than f(X 2 ).

In step S 14 : when f(X 1 ) is lower than f(X 2 ), L=X 1 , X 1 =X 2 , and X 2 =L+τ×(R−L). Wherein L≠X 1 represents that X 1 is assigned to L, X 1 =X 2 represents that X 2 is assigned to X 1 , and X 2 =L+τ×(R−L) represents that X 2 is defined via reassigned L.

In step S 15 : when f(X 1 ) is greater than f(X 2 ), R=X 2 , X 2 =X 1 , and X 1 =L+(1−τ)×(R−L). Wherein R=X 2 represents that X 2 is assigned to R, X 1 =X 2 represents that X 2 is assigned to X 1 , and X 1 =L+(1−τ)×(R−L) represents that X 1 is defined via reassigned R. In the embodiment, R=1.3124, X 2 =0.8696, and X 1 =0.6138.

In step S 16 : L≠InitL and R≠InitR are determined whether or not are established. When L≠InitL and R≠InitR are unestablished, returning to step S 13 . In this embodiment, as the value of L is unchanged, L≠InitL and R≠InitR are unestablished.

In step S 17 : when L≠InitL and R≠InitR are established, the focused positions L, X 1 , X 2 , and R and the resolutions f(L), f(X 1 ), f(X 2 ), and f(R) of images captured by the lens module 100 when the focusing lens 10 are positioned on the focused positions L, X 1 , X 2 , and R are acquired. It should understand that, the step S 14 and the step S 15 will respectively be run at least one time when L≠InitL and R≠InitR are established.

In step S 18 : an optimal focused position X p is calculated according to the focused positions L, X 1 , X 2 , and R and the resolutions f(L), f(X 1 ), f(X 2 ), and f(R). The calculating process comprises the steps:

In step S 181 : three groups of the focused positions and the resolutions are orderly sampled from the focused positions L, X 1 , X 2 , and R and the resolutions f(L), f(X 1 ), f(X 2 ), and f(R). Each three groups of the focused positions and the resolutions are substituted into a parabolic equation to calculate a peak coordinate. Therefore, four peak coordinates (X n1 , f(n1)), (X n2 , f(n2)), (X n3 , f(n3)), (X n4 , f(n4)) are calculated.

In step S182: the optimal focused position X p is acquired, wherein X p =(L*f(L)+X 1 *f(X 1 )+X 2 *f(X 2 )+R*f(R)+X n1 *f(n1)+X n2 *f(n2)+X n3 *f(n3)+X n4 *f(n4))/(L+X 1 +X 2 +R+X n1 +X n2 +X n3 +X n4 ).

It should be understood that, the optimal focused position X p can be calculated by other methods, such as X p =(L*f(L)+X 1 *f(X 1 )+X 2 *f(X 2 )+R*f(R))/(L+X 1 +X 2 +R), or X p =(X n1 *f(n1)+X n2 *f(n2)+X n3 *f(n3)+X n4 *f(n4))/(X n1 +X n2 +X n3 +X n4 ) .

The searching boundaries R, L move toward the optimal focused position X p by the golden section method, and the optimal focused position X p is calculated according to the focused positions L, X 1 , X 2 , and R and the resolutions f(L), f(X 1 ), f(X 2 ), and f(R). Therefore, the accuracy of the optimal focused position X p is increased.

Particular embodiments are shown and described by way of illustration only. The principles and the features of the present disclosure may be employed in various and numerous embodiments thereof without departing from the scope of the disclosure as claimed. The above-described embodiments illustrate the scope of the disclosure but do not restrict the scope of the disclosure.