12. depth and image restoration from light field in a scattering medium
Name: Zachary Paul Murez
Grad Year: 2018
Traditional imaging methods and computer vision algorithms are often ineffective when images are acquired in scattering media, such as underwater, fog, and biological tissue. Here, we explore the use of light ﬁeld imaging and algorithms for image restoration and depth estimation that address the image degradation from the medium. Towards this end, we make the following three contributions. First, we present a new single image restoration algorithm which removes backscatter and attenuation from images better than existing methods, and apply it to each view in the light ﬁeld. Second, we combine a novel transmission based depth cue with existing correspondence and defocus cues to improve light ﬁeld depth estimation. In densely scattering media, our transmission depth cue is critical for depth estimation since the images have low signal to noise ratios which signiﬁcantly degrades the performance of the correspondence and defocus cues. Finally, we propose shearing and refocusing multiple views of the light ﬁeld to recover a single image of higher quality than what is possible from a single view. We demonstrate the beneﬁts of our method through extensive experimental results in a water tank.