5,324 research outputs found
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction
Benefiting from the vigorous development of deep learning, many CNN-based
image super-resolution methods have emerged and achieved better results than
traditional algorithms. However, it is difficult for most algorithms to
adaptively adjust the spatial region and channel features at the same time, let
alone the information exchange between them. In addition, the exchange of
information between attention modules is even less visible to researchers. To
solve these problems, we put forward a lightweight spatial-channel adaptive
coordination of multilevel refinement enhancement networks(MREN). Specifically,
we construct a space-channel adaptive coordination block, which enables the
network to learn the spatial region and channel feature information of interest
under different receptive fields. In addition, the information of the
corresponding feature processing level between the spatial part and the channel
part is exchanged with the help of jump connection to achieve the coordination
between the two. We establish a communication bridge between attention modules
through a simple linear combination operation, so as to more accurately and
continuously guide the network to pay attention to the information of interest.
Extensive experiments on several standard test sets have shown that our MREN
achieves superior performance over other advanced algorithms with a very small
number of parameters and very low computational complexity
Image Super-resolution with An Enhanced Group Convolutional Neural Network
CNNs with strong learning abilities are widely chosen to resolve
super-resolution problem. However, CNNs depend on deeper network architectures
to improve performance of image super-resolution, which may increase
computational cost in general. In this paper, we present an enhanced
super-resolution group CNN (ESRGCNN) with a shallow architecture by fully
fusing deep and wide channel features to extract more accurate low-frequency
information in terms of correlations of different channels in single image
super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is
useful to inherit more long-distance contextual information for resolving
long-term dependency. An adaptive up-sampling operation is gathered into a CNN
to obtain an image super-resolution model with low-resolution images of
different sizes. Extensive experiments report that our ESRGCNN surpasses the
state-of-the-arts in terms of SISR performance, complexity, execution speed,
image quality evaluation and visual effect in SISR. Code is found at
https://github.com/hellloxiaotian/ESRGCNN
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