Restoration of linear motion and out-of-focus blurred images in surveillance systems

Linear motion and out-of-focus blur often coexist in a surveillance system, which degrade the quality of acquired images and thus complicate the task of object recognition and event detection. In this work, we present a point spread function-based (PSF-based) approach considering fundamental characteristics of linear motion and out-of-focus blur based on geometric optics to restore coexisting motion and out-of-focus blurred images without application-dependent parameters selection, where a sharpness measure is employed as a cost function to automatically select optimal parameter values for PSF. To verify the effectiveness of our proposed approach, we compare our approach with existing de-blur approaches. Experimental results shows our proposed method can automatically select optimal parameter values for PSF and outperform existing de-blur approaches.