White balance corrected image processing method and device based on gray edge constraint gray world

Abstract

The invention relates to the technical field of digital image processing, in particular to a white balance corrected image processing method and device based on gray edge constraint gray world, and solves the problems that the existing gray world algorithm is high in correctness in most scenes, but is unstable in partial scenes (such as large monochrome objects) and the gray edge algorithm is high in robustness but low in correctness. The method comprises the following steps of solving the gray edge constraint by the gray edge algorithm, limiting the solving space into the constrained range of the gray edge, and ensuring the basic robustness of the algorithm; and solving the exact solution in the limited solving space by the gray world algorithm. The disclosed algorithm avoids the disadvantages of two basic algorithms, i.e. the gray edge and the gray world, makes full use of the advantages of the two algorithms, can fast solve the exact solution, and is strong in robustness. The method and the device provided by the invention are reasonable in design.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image processing method and device for white balance correction based on gray edge constrained gray world. Background technique [0002] Color is the basis of an image, and it is also the visual information of an image. On the one hand, the color information of the image is collected for human viewing; on the other hand, as an important clue, the color information of the image is widely used in computer vision research, such as feature extraction, object recognition, image retrieval, etc. . However, under different lighting conditions, the color reflected by the object is different. The purpose of white balance is to eliminate the influence of different lighting and restore the true color of the object under standard lighting. [0003] Image illumination estimation is the first step in white balance calculation, and it is often the most important and difficu...

AI Technical Summary

Problems solved by technology

First, although the gray edge method can be realized with only a few lines of program codes, the calculation process involves the Gaussian kernel convolution process, which seriously affects the calculation speed of the algorithm. For example, the experiment found that for the second-order gray edge method, 4 <σ<7 works better. Assuming σ=4, the size of the convolution kernel is 25*25. Even if the convolution in the x direction and the y direction is decomposed, two convolutions of 1*25 size are required. This amount of calculation is equivalent to the calculation amount of doing the gray world algorithm 50 times

Second, the value of the σ and p parameters involved in this method, if the value is not appropriate, it is difficult to obtain better results, especially when there is no prior information about the input image

Third, the calculation of the image gradient of this method is complex and the amount of calculation is large. For example, the calculation formula of the first-order gradient is ∂ 1 f ( x ) ∂ x 1 = ( ∂ 1 + 0 f ( x ) ∂ x 1 y 0 ) 2 + ( ∂ 0 + 1 f ( x ) ∂ x 0 y 1 ) 2 , It is necessary to calculate the first-order gradient in the x direction first, and then calculate the first-order gradient in the y direction, which also involves square and square root operations, and the calculation of the second-order gradient is more complicated

The principle of the gray edge method is simple, and the effect is greatly improved, but its calculation process involves Gaussian convolution, the time complexity is high, and the selection of the convolution kernel size lacks specific guidance.

[0009] In short, the gray world algorithm has high accuracy in most scenes, but its performance is very unstable in some scenes (such as large-area monochrome objects); the gray edge algorithm has high robustness, but its accuracy is not high

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