A no-reference sharpness assessment method and system for whole slice images

Abstract

The present invention provides a reference-free evaluation method and system for a full-slice image, which uses a pyramid multi-layer data structure to store digital images, and divides each layer image of the pyramid multi-layer data structure into several image blocks on average; through Otsu threshold segmentation The method divides the strong edge in the gradient magnitude histogram of the image block, calculates the strong edge strength from the gradient maximum value and width of the strong edge, and uses the background complexity to correct the strong edge strength to determine the definition of a single image block; Set the standard value of sharpness to determine whether the current image block is a clear image or a blurred image; according to the proportion of clear image blocks in each layer, finally determine whether the image of each layer is a clear image. The invention avoids the difference of image clarity score caused by the difference of image content, corrects the influence of image background complexity on image clarity, and realizes the effective identification of partly clear and partly blurred images.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, in particular to a reference-free definition evaluation method and system for full-slice images. Background technique [0002] With the development of information technology, digital full-section pathological images have been widely used in the fields of clinical diagnosis and pathological research. Due to various uncertain factors in the process of slice making and microscopic scanning, the imaging quality of pathological slices is likely to decline. The decrease in image clarity not only directly affects the diagnostic quality of pathological slides, but also cannot be used as an important sample for intelligent diagnostic research such as data mining. Therefore, a fast and effective digital pathology image sharpness assessment method is crucial for the development of digital pathology. [0003] According to the degree of dependence on reference images, objective eval...

AI Technical Summary

Problems solved by technology

At present, the following deficiencies generally exist in the practical application of digital pathology image sharpness evaluation methods: 1) The sharpness is seriously affected by the image content. Under the same sharpness, images with complex content score significantly higher than images with simple content; 2) , the partially sharp and partially blurred image scores very close to the full sharp image

These problems make existing clarity assessment methods unsuitable for universal clarity assessment

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