Refocusing Metric of Light Field Image Using Region-Adaptive Multi-Scale Focus Measure

Abstract

Compared with conventional photography, the newly emerging light field image capturing technique has dramatically extended potential capabilities of post processing. Among the new capabilities, refocusing is of the most interest. In this paper, we first investigate a region-adaptive multi-scale focus measure (RA-MSFM) that is able to more robustly and accurately measure focus of light field images. It is especially superior when measuring focus in flat areas where previous methods struggle. Following we design a novel refocusing measure metric which employs the RA-MSFM as core technique. Using the metric, refocusing capability of a given light field image as a whole can be measured in a single number by combining focus score maps of each refocused image in the focal stack. The focus score maps are generated using the proposed RA-MSFM. In RA-MSFM, different multi-scale factor is adaptively selected depending on different regions such as texture-rich or flat areas using a multi-layer perceptron network. Different from most light field image metrics that assess image quality, our metric targets to assess refocusing capability. Our experiments have shown that not only does the proposed refocusing metric have high correlation with subjective evaluations given in the form of mean opinion scores, but it also produces all-in-focus images having 0.7 ~ 4.6dB higher PSNRs compared to previous state-of-the-art methods. The proposed refocusing metric can be used to measure refocusing loss in practical application such as compression, tone mapping, denoising, and smoothing.

Keywords

• Refocusing measure
• light field images
• multi-scale focus measure
• all-in-focus
• subjective experiment
• refocusing capability