Adaptive image acquisition by autodefocusing

Abstract

We present an adaptive image-acquisition methodology by replacing the traditional birefringent filter with slight out-of-focus blur generated by the camera lens. Because many cameras already have adjustable lenses and autofocus systems, our method can exploit existing hardware by simply changing the focusing strategy. During the image acquisition, the optimal defocus setting is automatically adapted to the power spectrum of the scene, which is evaluated by a generic autocorrelation model. We develop a criterion to estimate reconstruction errors without the baseband knowledge of the scene. This metric helps the camera to choose the optimal defocus settings. An optimal Wiener filter then recovers the captured scene and yields sharper images with reduced aliasing. The numerical and visual results show that our method is superior to current acquisition methods used by most digital cameras.