Abstract Focus misses are common in image capture, such as when the camera or the subject moves rapidly in sports and macro photography. One option to sharpen focus‐missed photographs is through single image deconvolution, but high‐frequency data cannot be fully recovered; therefore, artifacts such as ringing and amplified noise become apparent. We propose a new method that uses assisting, similar but different, sharp image(s) provided by the user (such as multiple images of the same subject in different positions captured using a burst of photographs). Our first contribution is to theoretically analyze the errors in three sources of data—a slightly sharpened original input image that we call the target , single image deconvolution with an aggressive inverse filter, and warped assisting image(s) registered using optical flow. We show that these three sources have different error characteristics, depending on image location and frequency band (for example, aggressive deconvolution is more accurate in high‐frequency regions like edges). Next, we describe a practical method to compute these errors, given we have no ground truth and cannot easily work in the Fourier domain. Finally, we select the best source of data for a given pixel and scale in the Laplacian pyramid. We accurately transfer high‐frequency data to the input, while minimizing artifacts. We demonstrate sharpened results on out‐of‐focus images in macro, sports, portrait and wildlife photography.
Vehicle trajectories contain rich information on microscopic phenomena such as car following and lane changing. Despite many efforts to retrieve reliable trajectories from video images, previous approaches do not give high enough quality of trajectories that can be used in microscopic analysis. We introduce a new vehicle tracking approach based on a model-based 3-D vehicle detection and description algorithm. The proposed algorithm uses a probabilistic line feature grouping method to detect vehicles with little computation. A dynamic programming algorithm is proposed for fast reasoning. We present the system implementation and the vehicle detection and tracking results.
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTKinetics of the Oxidation of Carbon Monoxide and the Decomposition of Carbon Dioxide in a Radiofrequency Electric Discharge. I. Experimental ResultsLloyd C. Brown and Alexis T. BellCite this: Ind. Eng. Chem. Fundamen. 1974, 13, 3, 203–210Publication Date (Print):August 1, 1974Publication History Published online1 May 2002Published inissue 1 August 1974https://pubs.acs.org/doi/10.1021/i160051a008https://doi.org/10.1021/i160051a008research-articleACS PublicationsRequest reuse permissionsArticle Views217Altmetric-Citations38LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options Get e-Alerts