31 research outputs found
Deep Networks for Image Super-Resolution with Sparse Prior
Deep learning techniques have been successfully applied in many areas of
computer vision, including low-level image restoration problems. For image
super-resolution, several models based on deep neural networks have been
recently proposed and attained superior performance that overshadows all
previous handcrafted models. The question then arises whether large-capacity
and data-driven models have become the dominant solution to the ill-posed
super-resolution problem. In this paper, we argue that domain expertise
represented by the conventional sparse coding model is still valuable, and it
can be combined with the key ingredients of deep learning to achieve further
improved results. We show that a sparse coding model particularly designed for
super-resolution can be incarnated as a neural network, and trained in a
cascaded structure from end to end. The interpretation of the network based on
sparse coding leads to much more efficient and effective training, as well as a
reduced model size. Our model is evaluated on a wide range of images, and shows
clear advantage over existing state-of-the-art methods in terms of both
restoration accuracy and human subjective quality
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