The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE, Absolute Mean Brightness Coefficientt (AMBC) and Peak Signal-to-Noise Ratio (PSNR).
This paper attempts to use improved image segmentation algorithm based on maximum entropy method in image recognition of Lingwu long jujubes, which provides more selection and comparison for image segmentation algorithm of Lingwu long jujubes in the process of automatic picking.
Specifically, compared to the wavelet-based method [7, 8], which is computation expensive in decomposing the image into different frequency bands and computation of transform, the proposed image definition measure based on fuzzy entropy is more efficient and applicable in practical image measurement system.
First, a new method is proposed to model the complex imaging process and evaluate the image definitions based on fuzzy entropy.
In [10], Yong et al. propose the algorithm for multi-source image matching based on information entropy which comprehensively considers of the intensity information and the edge direction information.
A probability value is calculated for each partition to determine the entropy based on Shannon's entropy [ 28]. 4. Parametric graph entropy measures called Dehmer-entropy: To determine the entropy, measures of this class [ 8, 10], assign a probability value to each vertex of a graph, using so-called information functionals (IFs).
To evaluate the image definition, we compute an image contrast map based on the fuzzy entropy defined above firstly, because the image contrast is closely related to the image definition.
We considered two types of "strong" indications as support, one type based on entropy and one based on lift.
Compared to the traditional methods, the proposed image definition measure based on fuzzy entropy has two important advantages.
The distance z between each de-focal plane and the focal plane is determined automatically by searching for the clearest recovered image | U (x, y ) | via an image-definition-evaluation criterion based on information entropy theory (see Subsection 2.2).
