Combining Inertial Measurements With Blind Image Deblurring Using Distance Transform

Camera motion during exposure leads to a blurry image. We propose an image deblurring method that infers the blur kernel by combining the inertial measurement unit (IMU) data that track camera motion with techniques that seek blur cues from the image sensor data. Specifically, we introduce the notion of IMU fidelity cost designed to penalize blur kernels that are unlikely to have yielded the observed IMU measurements. When combined with the image data-based fidelity and regularization terms used by the conventional blind image deblurring techniques, the overall energy function is nonconvex. We solved this nonconvex energy minimization problem by a novel use of distance transform, recovering a blur kernel and sharp image that are consistent with the IMU and image sensor measurements.