6,221 research outputs found
State Classification of Cooking Objects Using a VGG CNN
In machine learning, it is very important for a robot to know the state of an
object and recognize particular desired states. This is an image classification
problem that can be solved using a convolutional neural network. In this paper,
we will discuss the use of a VGG convolutional neural network to recognize
those states of cooking objects. We will discuss the uses of activation
functions, optimizers, data augmentation, layer additions, and other different
versions of architectures. The results of this paper will be used to identify
alternatives to the VGG convolutional neural network to improve accuracy.Comment: 5 Pages, 4 Figure
Does Haze Removal Help CNN-based Image Classification?
Hazy images are common in real scenarios and many dehazing methods have been
developed to automatically remove the haze from images. Typically, the goal of
image dehazing is to produce clearer images from which human vision can better
identify the object and structural details present in the images. When the
ground-truth haze-free image is available for a hazy image, quantitative
evaluation of image dehazing is usually based on objective metrics, such as
Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in
many applications, large-scale images are collected not for visual examination
by human. Instead, they are used for many high-level vision tasks, such as
automatic classification, recognition and categorization. One fundamental
problem here is whether various dehazing methods can produce clearer images
that can help improve the performance of the high-level tasks. In this paper,
we empirically study this problem in the important task of image classification
by using both synthetic and real hazy image datasets. From the experimental
results, we find that the existing image-dehazing methods cannot improve much
the image-classification performance and sometimes even reduce the
image-classification performance
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
The existence of hybrid noise in hyperspectral images (HSIs) severely
degrades the data quality, reduces the interpretation accuracy of HSIs, and
restricts the subsequent HSIs applications. In this paper, the spatial-spectral
gradient network (SSGN) is presented for mixed noise removal in HSIs. The
proposed method employs a spatial-spectral gradient learning strategy, in
consideration of the unique spatial structure directionality of sparse noise
and spectral differences with additional complementary information for better
extracting intrinsic and deep features of HSIs. Based on a fully cascaded
multi-scale convolutional network, SSGN can simultaneously deal with the
different types of noise in different HSIs or spectra by the use of the same
model. The simulated and real-data experiments undertaken in this study
confirmed that the proposed SSGN performs better at mixed noise removal than
the other state-of-the-art HSI denoising algorithms, in evaluation indices,
visual assessments, and time consumption.Comment: Accept by IEEE TGR
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
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network
Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images
is a critical step in medical image analysis. Over the past few years, many
algorithms with impressive performances have been proposed. In this paper,
inspired by the idea of deep learning, we introduce an MRI denoising method
based on the residual encoder-decoder Wasserstein generative adversarial
network (RED-WGAN). Specifically, to explore the structure similarity between
neighboring slices, a 3D configuration is utilized as the basic processing
unit. Residual autoencoders combined with deconvolution operations are
introduced into the generator network. Furthermore, to alleviate the
oversmoothing shortcoming of the traditional mean squared error (MSE) loss
function, the perceptual similarity, which is implemented by calculating the
distances in the feature space extracted by a pretrained VGG-19 network, is
incorporated with the MSE and adversarial losses to form the new loss function.
Extensive experiments are implemented to assess the performance of the proposed
method. The experimental results show that the proposed RED-WGAN achieves
performance superior to several state-of-the-art methods in both simulated and
real clinical data. In particular, our method demonstrates powerful abilities
in both noise suppression and structure preservation.Comment: To appear on Medical Image Analysis. 29 pages, 15 figures, 7 table
Variational based Mixed Noise Removal with CNN Deep Learning Regularization
In this paper, the traditional model based variational method and learning
based algorithms are naturally integrated to address mixed noise removal
problem. To be different from single type noise (e.g. Gaussian) removal, it is
a challenge problem to accurately discriminate noise types and levels for each
pixel. We propose a variational method to iteratively estimate the noise
parameters, and then the algorithm can automatically classify the noise
according to the different statistical parameters. The proposed variational
problem can be separated into regularization, synthesis, parameter estimation
and noise classification four steps with the operator splitting scheme. Each
step is related to an optimization subproblem. To enforce the regularization,
the deep learning method is employed to learn the natural images priori.
Compared with some model based regularizations, the CNN regularizer can
significantly improve the quality of the restored images. Compared with some
learning based methods, the synthesis step can produce better reconstructions
by analyzing the recognized noise types and levels. In our method, the
convolution neutral network (CNN) can be regarded as an operator which
associated to a variational functional. From this viewpoint, the proposed
method can be extended to many image reconstruction and inverse problems.
Numerical experiments in the paper show that our method can achieve some
state-of-the-art results for mixed noise removal
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
This paper studies the challenging problem of fingerprint image denoising and
inpainting. To tackle the challenge of suppressing complicated artifacts (blur,
brightness, contrast, elastic transformation, occlusion, scratch, resolution,
rotation, and so on) while preserving fine textures, we develop a multi-scale
convolutional network, termed U- Finger. Based on the domain expertise, we show
that the usage of dilated convolutions as well as the removal of padding have
important positive impacts on the final restoration performance, in addition to
multi-scale cascaded feature modules. Our model achieves the overall ranking of
No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint
Denoising and Inpainting). Among all participating teams, we obtain the MSE of
0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the
hold-out testing set.Comment: ECCV 2018 Track-3 Challenge Inpainting to denoise fingerprin
Toward Convolutional Blind Denoising of Real Photographs
While deep convolutional neural networks (CNNs) have achieved impressive
success in image denoising with additive white Gaussian noise (AWGN), their
performance remains limited on real-world noisy photographs. The main reason is
that their learned models are easy to overfit on the simplified AWGN model
which deviates severely from the complicated real-world noise model. In order
to improve the generalization ability of deep CNN denoisers, we suggest
training a convolutional blind denoising network (CBDNet) with more realistic
noise model and real-world noisy-clean image pairs. On the one hand, both
signal-dependent noise and in-camera signal processing pipeline is considered
to synthesize realistic noisy images. On the other hand, real-world noisy
photographs and their nearly noise-free counterparts are also included to train
our CBDNet. To further provide an interactive strategy to rectify denoising
result conveniently, a noise estimation subnetwork with asymmetric learning to
suppress under-estimation of noise level is embedded into CBDNet. Extensive
experimental results on three datasets of real-world noisy photographs clearly
demonstrate the superior performance of CBDNet over state-of-the-arts in terms
of quantitative metrics and visual quality. The code has been made available at
https://github.com/GuoShi28/CBDNet
RARE: Image Reconstruction using Deep Priors Learned without Ground Truth
Regularization by denoising (RED) is an image reconstruction framework that
uses an image denoiser as a prior. Recent work has shown the state-of-the-art
performance of RED with learned denoisers corresponding to pre-trained
convolutional neural nets (CNNs). In this work, we propose to broaden the
current denoiser-centric view of RED by considering priors corresponding to
networks trained for more general artifact-removal. The key benefit of the
proposed family of algorithms, called regularization by artifact-removal
(RARE), is that it can leverage priors learned on datasets containing only
undersampled measurements. This makes RARE applicable to problems where it is
practically impossible to have fully-sampled groundtruth data for training. We
validate RARE on both simulated and experimentally collected data by
reconstructing a free-breathing whole-body 3D MRIs into ten respiratory phases
from heavily undersampled k-space measurements. Our results corroborate the
potential of learning regularizers for iterative inversion directly on
undersampled and noisy measurements.Comment: In press for IEEE Journal of Special Topics in Signal Processin
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Image denoising techniques are essential to reducing noise levels and
enhancing diagnosis reliability in low-dose computed tomography (CT). Machine
learning based denoising methods have shown great potential in removing the
complex and spatial-variant noises in CT images. However, some residue
artifacts would appear in the denoised image due to complexity of noises. A
cascaded training network was proposed in this work, where the trained CNN was
applied on the training dataset to initiate new trainings and remove artifacts
induced by denoising. A cascades of convolutional neural networks (CNN) were
built iteratively to achieve better performance with simple CNN structures.
Experiments were carried out on 2016 Low-dose CT Grand Challenge datasets to
evaluate the method's performance.Comment: 9 pages, 9 figure
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