The goal of autofocus is to enable a digital camera to capture sharp images as accurately and quickly as possible in any lighting condition without human intervention. Recent developments in mobile imaging seek to embed phase-detection sensor pixels into the image sensor itself because these phase-detection sensors are able to provide information for controlling both the amount and the direction of lens offset and thereby expedite the autofocus process. Compared to the conventional contrast-detection autofocus algorithms, however, the presence of noise, the lack of contrast in the image, and the spatial offset between the left and right phase sensing pixels can easily affect phase detection. In this paper, we propose to address the issue by characterizing the relation between phase shift and lens movement for various object depths by a statistical model. Experiments are conducted to show that the proposed method is indeed able to improve the reliability of phase-detection autofocus.
This paper presents an efficient algorithm for motion estimation to reduce High Efficiency Video Coding (HEVC) standard encoding complexity. Phase correlation is initially utilized as a preprocessing step to indicate an approximation of the shift between coding units in the current frame and the reference frame. This is followed by a 9-point diamond search centered on the shift found in the initial step, in order to refine the best matching block. The proposed method has the potential to yield substantial improvements in terms of execution time and resulting video quality in comparison to the traditional search methods.