1,327 research outputs found
Visual-Quality-Driven Learning for Underwater Vision Enhancement
The image processing community has witnessed remarkable advances in enhancing
and restoring images. Nevertheless, restoring the visual quality of underwater
images remains a great challenge. End-to-end frameworks might fail to enhance
the visual quality of underwater images since in several scenarios it is not
feasible to provide the ground truth of the scene radiance. In this work, we
propose a CNN-based approach that does not require ground truth data since it
uses a set of image quality metrics to guide the restoration learning process.
The experiments showed that our method improved the visual quality of
underwater images preserving their edges and also performed well considering
the UCIQE metric.Comment: Accepted for publication and presented in 2018 IEEE International
Conference on Image Processing (ICIP
Self-Supervised Monocular Depth Underwater
Depth estimation is critical for any robotic system. In the past years
estimation of depth from monocular images have shown great improvement,
however, in the underwater environment results are still lagging behind due to
appearance changes caused by the medium. So far little effort has been invested
on overcoming this. Moreover, underwater, there are more limitations for using
high resolution depth sensors, this makes generating ground truth for learning
methods another enormous obstacle. So far unsupervised methods that tried to
solve this have achieved very limited success as they relied on domain transfer
from dataset in air. We suggest training using subsequent frames
self-supervised by a reprojection loss, as was demonstrated successfully above
water. We suggest several additions to the self-supervised framework to cope
with the underwater environment and achieve state-of-the-art results on a
challenging forward-looking underwater dataset
Haze visibility enhancement: A Survey and quantitative benchmarking
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are categorized into: multiple image methods, polarizing filter-based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well-known single-image methods, based on a recent dataset provided by Fattal (2014) and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.This study is supported by an Nvidia GPU Grant and a Canadian NSERC Discovery grant. R. T. Tan’s work in this research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centre in Singapore Funding Initiativ
Depth extraction from monocular video using bidirectional energy minimization and initial depth segmentation
In this paper, we propose to extract depth information from a monocular video sequence. When estimating the depth of the current frame, the bidirectional energy minimization in our scheme considers both the previous frame and next frame, which promises a much more robust depth map and reduces the problems associated with occlusion to a certain extent. After getting an initial depth map from bidirectional energy minimization, we further refine the depth map using segmentation by assuming similar depth values in one segmented region. Different from other segmentation algorithms, we use initial depth information together with the original color image to get more reliable segmented regions. Finally, detecting the sky region using a dark channel prior is employed to correct some possibly wrong depth values for outdoor video. The experimental results are much more accurate compared with the state-of-the-art algorithms
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