4,770 research outputs found
Optimized imaging using non-rigid registration
The extraordinary improvements of modern imaging devices offer access to data
with unprecedented information content. However, widely used image processing
methodologies fall far short of exploiting the full breadth of information
offered by numerous types of scanning probe, optical, and electron
microscopies. In many applications, it is necessary to keep measurement
intensities below a desired threshold. We propose a methodology for extracting
an increased level of information by processing a series of data sets
suffering, in particular, from high degree of spatial uncertainty caused by
complex multiscale motion during the acquisition process. An important role is
played by a nonrigid pixel-wise registration method that can cope with low
signal-to-noise ratios. This is accompanied by formulating objective quality
measures which replace human intervention and visual inspection in the
processing chain. Scanning transmission electron microscopy of siliceous
zeolite material exhibits the above-mentioned obstructions and therefore serves
as orientation and a test of our procedures
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
Recently, CNN based end-to-end deep learning methods achieve superiority in
Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Apart from that, existing popular Multi-scale approaches are runtime intensive
and memory inefficient. In this context, we proposed a fast Deep Multi-patch
Hierarchical Network to restore Non-homogeneous hazed images by aggregating
features from multiple image patches from different spatial sections of the
hazed image with fewer number of network parameters. Our proposed method is
quite robust for different environments with various density of the haze or fog
in the scene and very lightweight as the total size of the model is around 21.7
MB. It also provides faster runtime compared to current multi-scale methods
with an average runtime of 0.0145s to process 1200x1600 HD quality image.
Finally, we show the superiority of this network on Dense Haze Removal to other
state-of-the-art models.Comment: CVPR Workshops Proceedings 202
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
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