Blurred image detection method fusing frequency spectrum information and cepstrum information

Abstract

The invention discloses a blurred image detection method fusing frequency spectrum information and cepstrum information, belongs to the technical field of image processing, and particularly relates to the detection technology of various blurred images. According to the blurred image detection method, first, an energy frequency spectrum distribution feature and a singularity cepstrum value histogram feature of an image are calculated, and serve as blur features of the image; second, a support vector machine classifier is selected for differentiating sharp image features from the blur image features, and collected images with demarcated blur categories is used for training corresponding parameters of the support vector machine classifier; finally, the trained support vector machine classifier is used for detecting whether an image to be detected is a blurred image. The blurred image detection method has the advantage that as a non-reference blurred image detection method, the blurred image detection method needs no reference image, thereby being wide in application range; meanwhile, the defined blur features have specific physical significance, and therefore the sharp image and the blurred image can be differentiated accurately.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to the detection technology of various blurred images. Background technique [0002] Image blur detection has always been an important research direction in image processing and computer vision. It refers to the process of automatically screening out blurred images from the input image sequence. This technology is widely used in the fields of image processing and computer vision. For example, in license plate recognition, face recognition, object recognition and other problems, it is necessary to ensure that the input image is clear in order to further extract effective visual features for recognition. Therefore, in various computational vision and image processing problems, automatic deblurring of images is the most basic and crucial step. The current image blur detection algorithms can be divided into two categories: (1) blur detection methods with reference ...

AI Technical Summary

Problems solved by technology

Compared with the reference blurred image detection method, this kind of method is slightly insufficient in detection accuracy.

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