基于神经网络的图像局部模糊测量与分割-计算机应用技术专业论文.docx

AbstractAbstract Abstract Abstract The captured image be partially blurred very often，possibly due to the weather,camera sensor,illumination and the relatively fast motion between the camera and the object．Effective extraction of the partially blurred image of the blurred the image can be used for image restoration image fusion，etc．In this thesis， we mainly study the blur measurement and segmentation of the partially blurred image．The main contents follows： (1)Local blur measurement based on BP neural network There arc two limitations among existing blur metrics：1)It is easy to detect of fiat textured clear areas blurred areas；2)most existing blur metrics are only applicable to the global only suitable for measuring motion defocus blur．We propose BP neural network based image partial blur measurement method．In this method，a new unified blur feature based on all singular values and non-zero DCT coefficients is presented，which measures the sharpness from both spatial and frequency-domain．Different singular values reflect the distribution of different scale information and vary differently after blurring．The number of DCT coefficients depicts the information loss in the high frequency domain．Their combination can effectively capture the blurring effect in the flattened textured area． Then，BP neural network and support vector machine presented the classifiers to effectively measure the blur．Qualitative and Quantitative experiments with single multiple partially blurred imfiges show that the method effectively distinguish between the blurred and texture．flattened clear areas． (2)Image semantic based blurred region segmentation The existing blur measurement methods are difficult to accurately segment the blurred region．Thus，this thesis proposes a semantic segmentation method to extract the blurred region，with the observation that the partially blurred areas are generally semantic—consistent as patches．This method consists of three steps：1)a bilateral filter is applied fir