42 research outputs found
Single Image Super-Resolution Using Lightweight CNN with Maxout Units
Rectified linear units (ReLU) are well-known to be helpful in obtaining
faster convergence and thus higher performance for many deep-learning-based
applications. However, networks with ReLU tend to perform poorly when the
number of filter parameters is constrained to a small number. To overcome it,
in this paper, we propose a novel network utilizing maxout units (MU), and show
its effectiveness on super-resolution (SR) applications. In general, the MU has
been known to make the filter sizes doubled in generating the feature maps of
the same sizes in classification problems. In this paper, we first reveal that
the MU can even make the filter sizes halved in restoration problems thus
leading to compaction of the network sizes. To show this, our SR network is
designed without increasing the filter sizes with MU, which outperforms the
state of the art SR methods with a smaller number of filter parameters. To the
best of our knowledge, we are the first to incorporate MU into SR applications
and show promising performance results. In MU, feature maps from a previous
convolutional layer are divided into two parts along channels, which are then
compared element-wise and only their max values are passed to a next layer.
Along with some interesting properties of MU to be analyzed, we further
investigate other variants of MU and their effects. In addition, while ReLU
have a trouble for learning in networks with a very small number of
convolutional filter parameters, MU do not. For SR applications, our MU-based
network reconstructs high-resolution images with comparable quality compared to
previous deep-learning-based SR methods, with lower filter parameters.Comment: ACCV201
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
DehazeNet: An end-to-end system for single image haze removal
© 1992-2012 IEEE. Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
Neighbouring proximity: A key impact factor of deep machine learning
Deep Learning has become increasingly popular since 2006. It has an outstanding capability to extract and represent the features of raw data and it has been applied to many domains, such as image processing, pattern recognition, computer vision, machine translation, natural language processing, and autopilot. While the advantages of deep learning methods are widely accepted, the limitations are not well studied. This thesis studies cases where deep learning methods lose their advantages over traditional methods. Our experiments show that, when the neighbouring proximity disappears, deep learning methods are significantly less powerful than traditional methods. Our work not only clearly indicates that deep structure methods cannot fully replace traditional shallow methods but also shows the potential risks of applying deep learning to autopilot.deep learningimage processingpattern recognitioncomputer visionmachine translationnatural language processin