21,369 research outputs found
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs),
CNNs are successfully used for image denoising. However, they suffer from the
following drawbacks: (i) deep network architecture is very difficult to train.
(ii) Deeper networks face the challenge of performance saturation. In this
study, the authors propose a novel method called enhanced convolutional neural
denoising network (ECNDNet). Specifically, they use residual learning and batch
normalisation techniques to address the problem of training difficulties and
accelerate the convergence of the network. In addition, dilated convolutions
are used in the proposed network to enlarge the context information and reduce
the computational cost. Extensive experiments demonstrate that the ECNDNet
outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201
Detection of leaf structures in close-range hyperspectral images using morphological fusion
Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods
Efficient Bayesian-based Multi-View Deconvolution
Light sheet fluorescence microscopy is able to image large specimen with high
resolution by imaging the sam- ples from multiple angles. Multi-view
deconvolution can significantly improve the resolution and contrast of the
images, but its application has been limited due to the large size of the
datasets. Here we present a Bayesian- based derivation of multi-view
deconvolution that drastically improves the convergence time and provide a fast
implementation utilizing graphics hardware.Comment: 48 pages, 20 figures, 1 table, under review at Nature Method
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