SID: Sensor-assisted Image Deblurring System for Mobile Devices

Handheld mobile photography is often affected by motion blur due to the difficulty of keeping the camera's stable. The existing processing method is usually a high-cost deblurring process of a computer, which seriously affects the user experience, and the deblurring effect is poor due to the lack of information on camera motion. Inspire by edging computing's ongoing efforts in automatically and collaboratively process more types of resources in the edge and cloud. In this paper, we present SID, a sensor-assisted image deblurring system for mobile devices. Using information about camera motion acquired from built-in sensors from smartphones (e.g. accelerometers, gyroscopes, and magnetometers), then estimating the point spread function, and combining the image's segmentation smoothing characteristics with spatial adaptation to image Deblurring. The image is preprocessed by the p-m nonlinear diffusion model, which preserves the characteristics of the image. The fuzzy image confirmation avoids the damage to the original high quality image. Wiener-Hoff optimization is used to optimize the effect of image restoration. We evaluated 400 photos with varying degrees of blur and size. Compared to traditional blind and unblind deconvolution methods, our algorithm shows significant advantages in both deblurring and processing delays.