186 research outputs found
A Research and Strategy of Remote Sensing Image Denoising Algorithms
Most raw data download from satellites are useless, resulting in transmission
waste, one solution is to process data directly on satellites, then only
transmit the processed results to the ground. Image processing is the main data
processing on satellites, in this paper, we focus on image denoising which is
the basic image processing. There are many high-performance denoising
approaches at present, however, most of them rely on advanced computing
resources or rich images on the ground. Considering the limited computing
resources of satellites and the characteristics of remote sensing images, we do
some research on these high-performance ground image denoising approaches and
compare them in simulation experiments to analyze whether they are suitable for
satellites. According to the analysis results, we propose two feasible image
denoising strategies for satellites based on satellite TianZhi-1.Comment: 9 pages, 4 figures, ICNC-FSKD 201
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
A note on patch-based low-rank minimization for fast image denoising
Patch-based low-rank minimization for image processing attracts much
attention in recent years. The minimization of the matrix rank coupled with the
Frobenius norm data fidelity can be solved by the hard thresholding filter with
principle component analysis (PCA) or singular value decomposition (SVD). Based
on this idea, we propose a patch-based low-rank minimization method for image
denoising. The main denoising process is stated in three equivalent way: PCA,
SVD and low-rank minimization. Compared to recent patch-based sparse
representation methods, experiments demonstrate that the proposed method is
rather rapid, and it is effective for a variety of natural grayscale images and
color images, especially for texture parts in images. Further improvements of
this method are also given. In addition, due to the simplicity of this method,
we could provide an explanation of the choice of the threshold parameter,
estimation of PSNR values, and give other insights into this method.Comment: 4pages (two columns
Comparison of super-resolution algorithms applied to retinal images
A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations
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