The quality of veterinary B-ultrasound image is the only standard to identify the quality of veterinary B-ultrasound, and the clearer the image, the more requirements can be achieved in the actual operation.

The clarity of animal B-ultrasound digital image is becoming an important index to measure the quality of digital imaging system. At the same time, when we carry out fuzzy animal B-ultrasound image restoration, how to determine whether the restored animal B-ultrasound image has improved and the clarity has been improved compared with the original image are all related to how to evaluate the clarity of digital image objectively and effectively.

The specific parameters for evaluating the clarity of fuzzy animal B-ultrasound images formed by different reasons are also different. For defocusing fuzzy animal B-ultrasound images, the commonly used methods include image gray entropy method and image gray variance method. These two methods are simple to calculate, but the evaluation function formed by them has a gentle change curve near the focal plane, and there are two extreme points. The effect of these methods is not ideal when used in automatic focusing and other fields.

The sharpness evaluation functions are as follows: The sum of the absolute value of the gray difference of the neighborhood, this algorithm takes the sum of the absolute value of the gray difference of the adjacent pixels in four directions as the evaluation function; Roberts gradient sum, this algorithm is the use of pixel cross position relationship; The algorithm uses the sum of squares of the gray difference of two pixels adjacent to the x and y directions as the evaluation function; In this algorithm, the sum of squares of gray difference of 4 neighborhood pixels is used as the evaluation function.

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