Regional exposure algorithm based on weighted gray entropy difference

A gray entropy, sub-region technology, applied in the field of image processing, can solve problems such as information loss and inability to adjust, and achieve the effect of improving image quality, improving adaptability, and enhancing scene and local details

Inactive Publication Date: 2019-10-08
TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

The disadvantage of this method is that during the entire calculation process, all the details in the image cannot be adjusted to the best exposure effect relative to its sub-regions, resulting in the loss of information to a certain extent.
[0005] In summary, the existing exposure algorithms still have a lot of room for improvement in imaging

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  • Regional exposure algorithm based on weighted gray entropy difference
  • Regional exposure algorithm based on weighted gray entropy difference
  • Regional exposure algorithm based on weighted gray entropy difference

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Embodiment Construction

[0041] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0042] The regional exposure algorithm (REABW) based on the weighted gray entropy difference includes the following steps:

[0043] Step 1: In a fixed scene, calculate the weighted gray entropy difference U at each exposure time, and obtain the mapping relationship between the weighted gray entropy difference and the exposure time.

[0044] In this step, the formula for calculating the weighted gray entropy difference is as follows:

[0045]

[0046] G=|G mean -G median |

[0047] Among them, U is the weighted gray entropy difference, E is the information entropy, G is the gray offset, and G mean is the average gray value, G mean is the gray level median, d is the image depth, and the α weight value is set to 0.5. The exposure time is 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, the unit is ms, such as figure 1 shown.

[0048] Step 2: ...

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Abstract

The invention relates to a regional exposure algorithm based on weighted gray entropy difference, which is mainly and technically characterized by comprising the following steps of: calculating a weighted gray entropy difference U at each exposure time in a certain fixed scene to obtain a mapping relation between the weighted gray entropy difference and the exposure time; calculating a weighted gray entropy difference of the reference image, and obtaining exposure time by using a dichotomy; dividing the reference image into an over-exposure area Eh, an under-exposure area El and a normal exposure area A; calculating weighted gray entropy differences UA, Uh and U1 of each region; and finally, calculating the optimal exposure time corresponding to each region. The method is reasonable in design, can obtain a good exposure effect in most scenes, has good environment adaptability, can obtain a good imaging effect in complex scenes, and can be widely applied to the field of digital imaging.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a regional exposure algorithm (REABW) based on weighted gray entropy difference. Background technique [0002] Exposure algorithm is one of the indispensable modules of digital camera and camcorder imaging. A good exposure algorithm can make images in different scenes get proper exposure. In real life, there will be some complex light sources in the image acquisition scene, such as strong backlight and side light. In this case, an exposure algorithm with strong environmental adaptability is needed to solve the problem of inaccurate exposure of images in some areas caused by different light sources. The exposure algorithm generally selects the image gray value or image entropy as the exposure control parameter to adjust, and uses different strategies to adjust the exposure. [0003] At present, there are many kinds of exposure algorithms. In 2013, Yang Zuoting and oth...

Claims

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Application Information

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IPC IPC(8): H04N5/235
CPCH04N23/73
Inventor 胡晓彤刘楠朱博文
Owner TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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