Enhanced Raw Image Capture and Deblurring

Camera motion blur is a common problem in low-light imaging applications. Over the year numerous algorithms have been developed to deblur the image and provide a visually pleasing capture of the sensed scene. Many of these algorithms require that the blur kernel is known a priori which limits their application in many real-world situations. Recently, inertial sensors have been utilized in an attempt to estimate a blur kernel that can then be incorporated into deblurring algorithms to provide improved results. However, the effectiveness of these algorithms has been limited by lack of access to unprocessed raw image data obtained directly from the image sensor. In this thesis, we build an digital imaging system for the acquisition of raw im- age data in conjunction with 3-axis acceleration data. From the acceleration data camera motion during image capture can be estimated to provide information about blurring of the sensed scene. This blur kernel is used in a maximum a posteriori esti- mation algorithm to deblur the raw image data. Experiments demonstrate that the accelerometer-based deblurring algorithm on raw image data can generate improved results.