22,659 research outputs found
Reflection Separation and Deblurring of Plenoptic Images
In this paper, we address the problem of reflection removal and deblurring
from a single image captured by a plenoptic camera. We develop a two-stage
approach to recover the scene depth and high resolution textures of the
reflected and transmitted layers. For depth estimation in the presence of
reflections, we train a classifier through convolutional neural networks. For
recovering high resolution textures, we assume that the scene is composed of
planar regions and perform the reconstruction of each layer by using an
explicit form of the plenoptic camera point spread function. The proposed
framework also recovers the sharp scene texture with different motion blurs
applied to each layer. We demonstrate our method on challenging real and
synthetic images.Comment: ACCV 201
Underwater Stereo using Refraction-free Image Synthesized from Light Field Camera
There is a strong demand on capturing underwater scenes without distortions
caused by refraction. Since a light field camera can capture several light rays
at each point of an image plane from various directions, if geometrically
correct rays are chosen, it is possible to synthesize a refraction-free image.
In this paper, we propose a novel technique to efficiently select such rays to
synthesize a refraction-free image from an underwater image captured by a light
field camera. In addition, we propose a stereo technique to reconstruct 3D
shapes using a pair of our refraction-free images, which are central
projection. In the experiment, we captured several underwater scenes by two
light field cameras, synthesized refraction free images and applied stereo
technique to reconstruct 3D shapes. The results are compared with previous
techniques which are based on approximation, showing the strength of our
method.Comment: Accepted in 2019 IEEE International Conference on Image Processing
(ICIP
Single Image Reflection Removal with Physically-Based Training Images
Recently, deep learning-based single image reflection separation methods have
been exploited widely. To benefit the learning approach, a large number of
training image pairs (i.e., with and without reflections) were synthesized in
various ways, yet they are away from a physically-based direction. In this
paper, physically based rendering is used for faithfully synthesizing the
required training images, and a corresponding network structure and loss term
are proposed. We utilize existing RGBD/RGB images to estimate meshes, then
physically simulate the light transportation between meshes, glass, and lens
with path tracing to synthesize training data, which successfully reproduce the
spatially variant anisotropic visual effect of glass reflection. For guiding
the separation better, we additionally consider a module, backtrack network
(BT-net) for backtracking the reflections, which removes complicated ghosting,
attenuation, blurred and defocused effect of glass/lens. This enables obtaining
a priori information before having the distortion. The proposed method
considering additional a priori information with physically simulated training
data is validated with various real reflection images and shows visually
pleasant and numerical advantages compared with state-of-the-art techniques.Comment: Accepted to CVPR 2020, project page:
https://sgvr.kaist.ac.kr/~smkim/Reflection_removal_renderin
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
This paper proposes a deep neural network structure that exploits edge
information in addressing representative low-level vision tasks such as layer
separation and image filtering. Unlike most other deep learning strategies
applied in this context, our approach tackles these challenging problems by
estimating edges and reconstructing images using only cascaded convolutional
layers arranged such that no handcrafted or application-specific
image-processing components are required. We apply the resulting transferrable
pipeline to two different problem domains that are both sensitive to edges,
namely, single image reflection removal and image smoothing. For the former,
using a mild reflection smoothness assumption and a novel synthetic data
generation method that acts as a type of weak supervision, our network is able
to solve much more difficult reflection cases that cannot be handled by
previous methods. For the latter, we also exceed the state-of-the-art
quantitative and qualitative results by wide margins. In all cases, the
proposed framework is simple, fast, and easy to transfer across disparate
domains.Comment: Appeared at ICCV'17 (International Conference on Computer Vision
Single Image Reflection Removal Using Deep Encoder-Decoder Network
Image of a scene captured through a piece of transparent and reflective
material, such as glass, is often spoiled by a superimposed layer of reflection
image. While separating the reflection from a familiar object in an image is
mentally not difficult for humans, it is a challenging, ill-posed problem in
computer vision. In this paper, we propose a novel deep convolutional
encoder-decoder method to remove the objectionable reflection by learning a map
between image pairs with and without reflection. For training the neural
network, we model the physical formation of reflections in images and
synthesize a large number of photo-realistic reflection-tainted images from
reflection-free images collected online. Extensive experimental results show
that, although the neural network learns only from synthetic data, the proposed
method is effective on real-world images, and it significantly outperforms the
other tested state-of-the-art techniques
Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion
The light field faithfully records the spatial and angular configurations of
the scene, which facilitates a wide range of imaging possibilities. In this
work, we propose an LF synthesis algorithm which renders high quality novel LF
views far outside the range of angular baselines of the given references. A
stratified synthesis strategy is adopted which parses the scene content based
on stratified disparity layers and across a varying range of spatial
granularities. Such a stratified methodology proves to help preserve scene
structures over large perspective shifts, and it provides informative clues for
inferring the textures of occluded regions. A generative-adversarial network
model is further adopted for parallax correction and occlusion completion
conditioned on the stratified synthesis features. Experiments show that our
proposed model can provide more reliable novel view synthesis quality at large
baseline extension ratios. Over 3dB quality improvement has been achieved
against state-of-the-art LF view synthesis algorithms
Road Detection Technique Using Filters with Application to Autonomous Driving System
Autonomous driving systems are broadly used equipment in the industries and
in our daily lives, they assist in production, but are majorly used for
exploration in dangerous or unfamiliar locations. Thus, for a successful
exploration, navigation plays a significant role. Road detection is an
essential factor that assists autonomous robots achieved perfect navigation.
Various techniques using camera sensors have been proposed by numerous scholars
with inspiring results, but their techniques are still vulnerable to these
environmental noises: rain, snow, light intensity and shadow. In addressing
these problems, this paper proposed to enhance the road detection system with
filtering algorithm to overcome these limitations. Normalized Differences Index
(NDI) and morphological operation are the filtering algorithms used to address
the effect of shadow and guidance and re-guidance image filtering algorithms
are used to address the effect of rain and/or snow, while dark channel image
and specular-to-diffuse are the filters used to address light intensity
effects. The experimental performance of the road detection system with
filtering algorithms was tested qualitatively and quantitatively using the
following evaluation schemes: False Negative Rate (FNR) and False Positive Rate
(FPR). Comparison results of the road detection system with and without
filtering algorithm shows the filtering algorithm's capability to suppress the
effect of environmental noises because better road/non-road classification is
achieved by the road detection system. with filtering algorithm. This
achievement has further improved path planning/region classification for
autonomous driving systemComment: 7 pages, 7 figures, International Journal of Computing,
Communications & Instrumentation Engg. (IJCCIE) 201
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is emerging as a novel, minimally invasive diagnostic
technology for inspection of the GI tract and diagnosis of a wide range of
diseases and pathologies. Since the development of this technology, medical
device companies and many research groups have made substantial progress in
converting passive capsule endoscopes to robotic active capsule endoscopes with
most of the functionality of current active flexible endoscopes. However,
robotic capsule endoscopy still has some challenges. In particular, the use of
such devices to generate a precise three-dimensional (3D) mapping of the entire
inner organ remains an unsolved problem. Such global 3D maps of inner organs
would help doctors to detect the location and size of diseased areas more
accurately and intuitively, thus permitting more reliable diagnoses. To our
knowledge, this paper presents the first complete pipeline for a complete 3D
visual map reconstruction of the stomach. The proposed pipeline is modular and
includes a preprocessing module, an image registration module, and a final
shape-from-shading-based 3D reconstruction module; the 3D map is primarily
generated by a combination of image stitching and shape-from-shading
techniques, and is updated in a frame-by-frame iterative fashion via capsule
motion inside the stomach. A comprehensive quantitative analysis of the
proposed 3D reconstruction method is performed using an esophagus gastro
duodenoscopy simulator, three different endoscopic cameras, and a 3D optical
scanner
Automatic Layer Separation using Light Field Imaging
We propose a novel approach that jointly removes reflection or translucent
layer from a scene and estimates scene depth. The input data are captured via
light field imaging. The problem is couched as minimizing the rank of the
transmitted scene layer via Robust Principle Component Analysis (RPCA). We also
impose regularization based on piecewise smoothness, gradient sparsity, and
layer independence to simultaneously recover 3D geometry of the transmitted
layer. Experimental results on synthetic and real data show that our technique
is robust and reliable, and can handle a broad range of layer separation
problems.Comment: 9 pages, 9 figure
Generative Single Image Reflection Separation
Single image reflection separation is an ill-posed problem since two scenes,
a transmitted scene and a reflected scene, need to be inferred from a single
observation. To make the problem tractable, in this work we assume that
categories of two scenes are known. It allows us to address the problem by
generating both scenes that belong to the categories while their contents are
constrained to match with the observed image. A novel network architecture is
proposed to render realistic images of both scenes based on adversarial
learning. The network can be trained in a weakly supervised manner, i.e., it
learns to separate an observed image without corresponding ground truth images
of transmission and reflection scenes which are difficult to collect in
practice. Experimental results on real and synthetic datasets demonstrate that
the proposed algorithm performs favorably against existing methods
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