There are numerous passive contrast sensing autofocus algorithms that are well documented in literature, but some aspects of their comparative performance have not been widely researched. This study explores the relative merits of a set of autofocus algorithms via examining them against a variety of scene conditions. We create a statistics engine that considers a scene taken through a range of focal values and then computes the best focal position using each autofocus algorithm. The process is repeated across a survey of test scenes containing different representative conditions. The results are assessed against focal positions which are determined by manually focusing the scenes. Through examining these results, we then derive conclusions about the relative merits of each autofocus algorithm with respect to the criteria accuracy and unimodality. Our study concludes that the basic 2D spatial gradient measurement approaches yield the best autofocus results in terms of accuracy and unimodality.
Although its lens and image sensor fundamentally limit a digital still camera's imaging performance, image processing
can significantly improve the perceived quality of the output images. A well-designed processing pipeline achieves a
good balance between the available processing power and the image yield (the fraction of images that meet a minimum
quality criterion).
This paper describes the use of subjective and objective measurements to establish a methodology for evaluating the
image quality of processing pipelines. The test suite contains images both of analytical test targets for objective
measurements, and of scenes for subjective evaluations that cover the photospace for the intended application.
Objective image quality metrics correlating with perceived sharpness, noise, and color reproduction were used to
evaluate the analytical images. An image quality model estimated the loss in image quality for each metric, and the
individual metrics were combined to estimate the overall image quality. The model was trained with the subjective
image quality data.
The test images were processed through different pipelines, and the overall objective and subjective data was assessed
to identify those image quality metrics that exhibit significant correlation with the perception of image quality. This
methodology offers designers guidelines for effectively optimizing image quality.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.