Optimal defocus estimates from individual images for autofocusing a digital camera

Recently, we developed a method for optimally estimating focus error given a set of natural scenes, a waveoptics model of the lens system, a sensor array, and a specification of measurement noise. The method is based on first principles and can be tailored to any vision system for which these properties can be characterized. Here, the method is used to estimate defocus in local areas of images (64x64 pixels) formed in a Nikon D700 digital camera fitted with a 50mm Sigma prime lens. Performance is excellent. Defocus magnitude and sign can be estimated with high precision and accuracy over a wide range. The method takes an integrative approach that accounts for natural scene statistics and capitalizes (but not does depend exclusively) on chromatic aberrations. Although chromatic aberrations are greatly reduced in achromatic lenses, we show that there are sufficient residual chromatic aberrations in a high-quality prime lens for our method to achieve good performance. Our method has the advantages of both phase-detection and contrastmeasurement autofocus techniques, without their disadvantages. Like phase detection, the method provides point estimates of defocus (magnitude and sign), but unlike phase detection, it does not require specialized hardware. Like contrast measurement, the method is image-based and can operate in “Live View” mode, but unlike contrast measurement, it does not require an iterative search for best focus. The proposed approach could be used to develop improved autofocus algorithms for digital imaging and video systems.