An Adaptive Image Enhancement Method Based on Rhododendron Search Algorithm

Abstract

The invention discloses a method for adaptively enhancing images on the basis of a cuckoo search algorithm. The method has the advantages that a defined image quality evaluation function is utilized, information of spatial statistical features, entropy and the like of enhanced images is comprehensively considered, optimal parameter problems during application of a normalized incomplete Beta function to enhancing the images are optimally solved by the aid of the cuckoo search algorithm, accordingly, the optimal enhancement parameters can be quickly acquired, and the method can be applied to relevant technical fields of digital image processing; the optimal image enhancement parameters of the normalized incomplete Beta function can be quickly acquired by the aid of the method, the quality of the images can be objectively evaluated by the aid of the defined image quality evaluation function, accordingly, the method can be used for evaluating the quality of images after the excessively bright or dark images are enhanced, and the requirement on automatic image enhancement can be met.

Description

technical field [0001] The invention belongs to the cross-application field of intelligent computing and digital image processing, and in particular relates to an adaptive image enhancement method based on a cuckoo search algorithm. Background technique [0002] The quality of an image usually decreases due to effects such as uneven illumination during the process of digital image acquisition, and the image usually appears dark or bright. This creates a certain obstacle to extracting the required image information. Therefore, usually before the image is analyzed and processed, the image needs to be enhanced. Image enhancement is one of the basic steps in image processing. The main purpose is to enhance the brightness and contrast of an image, thereby highlighting certain information in an image while weakening or removing some unnecessary information. In the field of digital image processing, there are usually two kinds of practical enhancement methods: methods based on sp...

AI Technical Summary

Problems solved by technology

However, it is found in actual research and practice that the above methods have certain limitations and are not robust.

For example, although the linear stretching method is simple, it requires manual intervention, and the enhancement effect directly depends on the experience of the operator, so the result is difficult to guarantee

Histogram equalization is difficult to achieve ideal results for partial bright or dark image enhancement, and it will amplify noise, because digital images are discrete. For the histogram specification algorithm, due to the error caused by the approximate operation, the specification can only be close to the reference Histogram, the enhancement effect is also difficult to guarantee, Laplace sharpening only enhances the edge part and cannot improve the brightness and contrast of the image

In addition, the above method is mainly aimed at enhancing the global information of the image, and the effect of enhancing the local information of the image is poor.

[0003] In response to this problem, Tubbs proposed a normalized incomplete Beta function that can automatically fit typical grayscale transformation functions. Using this function for image enhancement has a good effect, but a reasonable selection of the enhanced image quality evaluation function and The normalized incomplete Beta function parameter is the key and difficulty of the algorithm, which requires more manual intervention and lacks adaptability and intelligence

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