A New Full-Reference Image Quality Metric for Motion Blur Profile Characterization

Motion blur is common in images captured by handheld devices, arising from hand, device and/or object motion. To restore sharp images from the images degraded by the motion, it is extremely important to assess the quality of the captured image and its corresponding blur profile as accurately as possible. In image deblurring, the perceived image quality is usually assessed by the SSIM and the PSNR metric. These methods have certain limitations and the objective image quality assessed by these methods can be contradictory to the subjectively perceived image quality. We propose a new reference image based objective blur level (BL) metric by utilizing point spread function/blur kernel analysis in this paper. In our experiments, we found our BL metric describes the perceived image quality of motion blurred images better than SSIM and PSNR in most cases. Additionally, our method performs well in low light and low texture images, where SSIM and PSNR metrics are prone to failure in describing blurriness/sharpness of the image.