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Adaptive Hyperspectral Mixed Noise Removal
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018This paper proposes a new denoising method for hyperspectral images (HSIs) corrupted by mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The proposed method has three distinctive features: 1) it exploits the intrinsic characteristics of HSIs, namely, low-rank and self-similarity; 2) the observation noise is ...
Tai-Xiang Jiang +3 more
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AIP Conference Proceedings, 2006
The relative motion of the LISA spacecraft will Doppler shift the laser frequencies by around 15 MHz. These Doppler shifts introduce sensitivity to the phase noise of the measurement’s master clock. Using the most stable clocks available this effect would degrade the LISA sensitivity by more than a factor of 100. This clock noise can be removed in post‐
William Klipstein +4 more
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The relative motion of the LISA spacecraft will Doppler shift the laser frequencies by around 15 MHz. These Doppler shifts introduce sensitivity to the phase noise of the measurement’s master clock. Using the most stable clocks available this effect would degrade the LISA sensitivity by more than a factor of 100. This clock noise can be removed in post‐
William Klipstein +4 more
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Histogram-Based Gaussian Noise Removal
International Journal of Applied Research on Information Technology and Computing, 2014In modern science and technology, as the digital image processing gets more and more importance, the process of image quality enhancement and image restoration becomes a matter of concern for the researchers. In an image, denoising complexity increases from salt and pepper impulse noise to random-valued impulse noise, through to Gaussian noise. As salt
Manohar Koli, S. Balaji
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Edge preserving mixed noise removal
Multimedia Tools and Applications, 2018To faithfully recover the clean images corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN), a novel edge preserving image denoising algorithm is proposed. The low- and high-frequency components of the image are restored separately.
Fenghua Guo, Caiming Zhang
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A noise estimation method for multiplicative noise removal
Computational and Applied MathematicszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bao Chen, Yuchao Tang, Xiaohua Ding
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Noise Removal With Filtering Techniques
2021An overview of the image noise models and the de-noising techniques available are presented here. Basically, filtering is one of the de-noising approaches that is normally performed in both spatial and frequency domains. Thus, this chapter focuses on these two approaches.
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Real-time impulse noise removal
Journal of Real-Time Image Processing, 2018An adaptive interpolation-based impulse noise removal (AIBINR) algorithm is proposed to remove impulse noise from color and gray-scale images in real time. AIBINR works fast and has no need for parameter tuning to remove fixed-valued impulse noise. A GPU application has been developed to demonstrate the speed and inherent parallelization capabilities ...
Alpaslan Gökcen, Cem Kalyoncu
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Noise fingerprint-based infrared fixed pattern noise removal
Applied OpticsFixed pattern noise (FPN) is a common artifact in digital infrared sensors, caused by inherent manufacturing imperfections, and often leads to severe image quality degradation. In this paper, we propose a novel, to our knowledge, method for FPN removal based on camera noise fingerprints, termed noise fingerprint guided denoising network (NFGD-Net). The
Tingjie Huang +3 more
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Noise suppression by removing singularities
IEEE Transactions on Signal Processing, 2000A method is proposed for suppressing Gaussian noise by extracting local singularities. The method is based on truncating Riemann series decomposition, whose components naturally characterize different orders of Holder regularity. The approach yields a single-step filtering technique whose performance is comparable to three-step wavelet decomposition ...
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Noise Removal by Nonlinear Synapses
1998When many synaptic inputs convergence onto a neuron the pooled noise can overwhelm sparse input signals. However, thresholding the inputs can remove noise and signal quality can be maintained. We present here how this mechanism applies to photon detection in the retina.
M. C. W. van Rossum, R. G. Smith
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