Abstract
In this paper, we propose a method that removes raindrops with light field image using image inpainting. We first use the depth map generated from light field image to detect raindrop regions which are then expressed as a binary mask. The original image with raindrops is improved by refocusing on the far regions and filtering by a high-pass filter. With the binary mask and the enhanced image, image inpainting is then utilized to eliminate raindrops from the original image. We compare pre-trained models of several deep learning based image inpainting methods. A light field raindrop dataset is released to verify our method. Image quality analysis is performed to evaluate the proposed image restoration method. The recovered images are further applied to object detection and visual localization tasks.
Highlights
Object detection and self-localization are the fundamental tasks in autonomous driving as well as mobile robotics
We propose a method that can effectively remove raindrops from the images captured by a handheld light field camera using image inpainting
Ng et al [7] simplified the design of a plenoptic camera and made a handheld light field camera based on a conventional camera
Summary
Object detection and self-localization are the fundamental tasks in autonomous driving as well as mobile robotics. Raindrops falling on vehicle windows (in case of built-in camera) or camera lenses (in case of external camera) on a rainy day are one of them They typically cause vision sensors to produce blurry images which in turn interfere with high-level environmental perception tasks. Based on this principle, Wilburn et al [13] captured light field by an array with different numbers of cameras. Ng et al [7] simplified the design of a plenoptic camera and made a handheld light field camera based on a conventional camera
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