Robust blind motion deblurring using near-infrared flash image

In light-limited situations, camera motion blur is one of the prime causes for poor image quality. Recovering the blur kernel and latent image from the blurred observation is an inherently ill-posed problem. In this paper, we introduce a hand-held multispectral camera to capture a pair of blurred image and Near-InfraRed (NIR) flash image simultaneously and analyze the correlation between the pair of images. To utilize the high-frequency details of the scene captured by the NIR-flash image, we exploit the NIR gradient constraint as a new type of image regularization, and integrate it into a Maximum-A-Posteriori (MAP) problem to iteratively perform the kernel estimation and image restoration. We demonstrate our method on the synthetic and real images with both spatially invariant and spatially varying blur. The experiments strongly support the effectiveness of our method to provide both accurate kernel estimation and superior latent image with more details and fewer ringing artifacts.