9,925 research outputs found
Image Super-Resolution Using TV Priori Guided Convolutional Network
We proposed a TV priori information guided deep learning method for single
image super-resolution(SR). The new alogorithm up-sample method based on TV
priori, new learning method and neural networks architecture are embraced in
our TV guided priori Convolutional Neural Network which diretcly learns an end
to end mapping between the low level to high level images.Comment: This paper is underviewring in Journal of Pattern Recognition Letter
Rain O'er Me: Synthesizing real rain to derain with data distillation
We present a supervised technique for learning to remove rain from images
without using synthetic rain software. The method is based on a two-stage data
distillation approach: 1) A rainy image is first paired with a coarsely
derained version using on a simple filtering technique ("rain-to-clean"). 2)
Then a clean image is randomly matched with the rainy soft-labeled pair.
Through a shared deep neural network, the rain that is removed from the first
image is then added to the clean image to generate a second pair
("clean-to-rain"). The neural network simultaneously learns to map both images
such that high resolution structure in the clean images can inform the
deraining of the rainy images. Demonstrations show that this approach can
address those visual characteristics of rain not easily synthesized by software
in the usual way
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
Dense depth cues are important and have wide applications in various computer
vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth
measurements around the vehicle to perceive the surrounding environments.
However, depth maps obtained by LIDAR are generally sparse because of its
hardware limitation. The task of depth completion attracts increasing
attention, which aims at generating a dense depth map from an input sparse
depth map. To effectively utilize multi-scale features, we propose three novel
sparsity-invariant operations, based on which, a sparsity-invariant multi-scale
encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature
maps is also proposed. Additional RGB features could be incorporated to further
improve the depth completion performance. Our extensive experiments and
component analysis on two public benchmarks, KITTI depth completion benchmark
and NYU-depth-v2 dataset, demonstrate the effectiveness of the proposed
approach. As of Aug. 12th, 2018, on KITTI depth completion leaderboard, our
proposed model without RGB guidance ranks first among all peer-reviewed methods
without using RGB information, and our model with RGB guidance ranks second
among all RGB-guided methods.Comment: IEEE Trans. on Image Processin
Perceptual deep depth super-resolution
RGBD images, combining high-resolution color and lower-resolution depth from
various types of depth sensors, are increasingly common. One can significantly
improve the resolution of depth maps by taking advantage of color information;
deep learning methods make combining color and depth information particularly
easy. However, fusing these two sources of data may lead to a variety of
artifacts. If depth maps are used to reconstruct 3D shapes, e.g., for virtual
reality applications, the visual quality of upsampled images is particularly
important. The main idea of our approach is to measure the quality of depth map
upsampling using renderings of resulting 3D surfaces. We demonstrate that a
simple visual appearance-based loss, when used with either a trained CNN or
simply a deep prior, yields significantly improved 3D shapes, as measured by a
number of existing perceptual metrics. We compare this approach with a number
of existing optimization and learning-based techniques.Comment: 26 page
Neural Nearest Neighbors Networks
Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at
https://github.com/visinf/n3net
Pixel-Adaptive Convolutional Neural Networks
Convolutions are the fundamental building block of CNNs. The fact that their
weights are spatially shared is one of the main reasons for their widespread
use, but it also is a major limitation, as it makes convolutions content
agnostic. We propose a pixel-adaptive convolution (PAC) operation, a simple yet
effective modification of standard convolutions, in which the filter weights
are multiplied with a spatially-varying kernel that depends on learnable, local
pixel features. PAC is a generalization of several popular filtering techniques
and thus can be used for a wide range of use cases. Specifically, we
demonstrate state-of-the-art performance when PAC is used for deep joint image
upsampling. PAC also offers an effective alternative to fully-connected CRF
(Full-CRF), called PAC-CRF, which performs competitively, while being
considerably faster. In addition, we also demonstrate that PAC can be used as a
drop-in replacement for convolution layers in pre-trained networks, resulting
in consistent performance improvements.Comment: CVPR 2019. Video introduction: https://youtu.be/gsQZbHuR64
"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors
Many seemingly unrelated computer vision tasks can be viewed as a special
case of image decomposition into separate layers. For example, image
segmentation (separation into foreground and background layers); transparent
layer separation (into reflection and transmission layers); Image dehazing
(separation into a clear image and a haze map), and more. In this paper we
propose a unified framework for unsupervised layer decomposition of a single
image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown
[Ulyanov et al] that the structure of a single DIP generator network is
sufficient to capture the low-level statistics of a single image. We show that
coupling multiple such DIPs provides a powerful tool for decomposing images
into their basic components, for a wide variety of applications. This
capability stems from the fact that the internal statistics of a mixture of
layers is more complex than the statistics of each of its individual
components. We show the power of this approach for Image-Dehazing, Fg/Bg
Segmentation, Watermark-Removal, Transparency Separation in images and video,
and more. These capabilities are achieved in a totally unsupervised way, with
no training examples other than the input image/video itself.Comment: Project page: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP
Light Weight Color Image Warping with Inter-Channel Information
Image warping is a necessary step in many multimedia applications such as
texture mapping, image-based rendering, panorama stitching, image resizing and
optical flow computation etc. Traditionally, color image warping interpolation
is performed in each color channel independently. In this paper, we show that
the warping quality can be significantly enhanced by exploiting the
cross-channel correlation. We design a warping scheme that integrates
intra-channel interpolation with cross-channel variation at very low
computational cost, which is required for interactive multimedia applications
on mobile devices. The effectiveness and efficiency of our method are validated
by extensive experiments
Real Image Denoising with Feature Attention
Deep convolutional neural networks perform better on images containing
spatially invariant noise (synthetic noise); however, their performance is
limited on real-noisy photographs and requires multiple stage network modeling.
To advance the practicability of denoising algorithms, this paper proposes a
novel single-stage blind real image denoising network (RIDNet) by employing a
modular architecture. We use a residual on the residual structure to ease the
flow of low-frequency information and apply feature attention to exploit the
channel dependencies. Furthermore, the evaluation in terms of quantitative
metrics and visual quality on three synthetic and four real noisy datasets
against 19 state-of-the-art algorithms demonstrate the superiority of our
RIDNet.Comment: Accepted in ICCV (Oral), 201
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive
discussion and exploration about how low-level vision techniques can benefit
the high-level automatic visual recognition in various scenarios. In its second
track, we focus on object or face detection in poor visibility enhancements
caused by bad weathers (haze, rain) and low light conditions. While existing
enhancement methods are empirically expected to help the high-level end task,
that is observed to not always be the case in practice. To provide a more
thorough examination and fair comparison, we introduce three benchmark sets
collected in real-world hazy, rainy, and low-light conditions, respectively,
with annotate objects/faces annotated. To our best knowledge, this is the first
and currently largest effort of its kind. Baseline results by cascading
existing enhancement and detection models are reported, indicating the highly
challenging nature of our new data as well as the large room for further
technical innovations. We expect a large participation from the broad research
community to address these challenges together.Comment: A summary paper on datasets, fact sheets, baseline results, challenge
results, and winning methods in UG Challenge (Track 2). More materials
are provided in http://www.ug2challenge.org/index.htm
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