Date of Award

Spring 1-1-2017

Document Type

Thesis

Degree Name

Master of Engineering (ME)

First Advisor

Moncef Krarti

Second Advisor

C. Walter Beamer IV

Third Advisor

Sandra Vasconez

Abstract

The presence of lens flare has been identified as a limitation associated with luminance measurements obtained using High Dynamic Range Imaging (HDRI) technology. Current documentation is lacking on the magnitude of error lens flare has on luminance values as well as the methods for lens flare removal. The purpose of this study is to provide an evaluation of the effectiveness of two existing lens flare removal techniques. The first technique involves the implementation of blind deconvolution during image postprocessing within commonly used HDRI software. The second technique aims to separate and extract lens flare from luminance values which are true to the scene using a high frequency occlusion mask. The results from this study indicated that while visually problematic, the error in luminance values resulting from lens flare effects a limited number of pixels located near the source. Of those pixels exhibiting significant error, current lens flare removal techniques are not sufficient at correcting this error nor do they adequately remove the visual appearance of lens flare found in HDR images. Future correction efforts may be best served by taking a step away from existing methods and working to create a more robust solution through the development of a Convolutional Neural Network (CNN).

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