Journal ArticleDOI
Infrared image denoising based on the variance-stabilizing transform and the dual-domain filter
TLDR
Zhang et al. as mentioned in this paper proposed a denoising method based on the variance-stabilizing transform (VST) and the dual-domain filter (DDF).About:
This article is published in Digital Signal Processing.The article was published on 2021-06-01. It has received 11 citations till now. The article focuses on the topics: Gaussian noise & Noise reduction.read more
Citations
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Journal ArticleDOI
Application and improvement of Canny edge-detection algorithm for exterior wall hollowing detection using infrared thermal images
TL;DR: Wang et al. as discussed by the authors developed the Canny algorithm to realize the automatic processing using the computer instead of manual judgment, and the proposed algorithm presented a better performance in the identification of hollowing edge contour according to the verification based on three cases.
Journal ArticleDOI
Thermal-Inertial SLAM for the Environments With Challenging Illumination
TL;DR: In this paper, a thermal-inertial SLAM method for all-day autonomous systems is proposed, which exploits the fixed noise pattern of thermal images and enhances the image quality to improve the performance of subsequent steps, including thermal feature extraction and loop detection.
Journal ArticleDOI
NSTBNet: Toward a nonsubsampled shearlet transform for broad convolutional neural network image denoising
TL;DR: Wang et al. as mentioned in this paper proposed a novel network integrated with nonsubsampled shearlet transform (NSST) and a broad convolutional neural network, namely NSTBNet, which has the following desirable properties: (1) a single model has the ability to deal with different noise levels and spatially variant additive Gaussian noise (AWGN); (2) the model combines the two networks make it wider to improve denoising performance without increasing too much computational cost; (3) the NSST and inverse NSST are employed to acquire more texture and to avoid gridding effect.
Journal ArticleDOI
DPNet: Detail-preserving image deraining via learning frequency domain knowledge
TL;DR: DPNet as discussed by the authors proposes a frequency domain residual block (FRDB) to fuse spatial and frequency domain features to restore details by reducing the differences in high-frequency space, and a simple detail enhancement attention module (DEAM) is used to further enhance the image details.
Journal ArticleDOI
A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain
TL;DR: In this article, a new method of crop image denoising with improved SVD in wavelet domain was proposed, which carried out a 3-layer wavelet transform on the crop noise image, leaving the low-frequency subimage unchanged; then, for the highfrequency subimages distributed in the horizontal, vertical, and diagonal directions, the improved adaptive SVD algorithm was used to filter the noise.
References
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Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Journal ArticleDOI
Nonlocally Centralized Sparse Representation for Image Restoration
TL;DR: The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, and the extensive experiments validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
Journal ArticleDOI
Two-stage image denoising by principal component analysis with local pixel grouping
TL;DR: Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art Denoising algorithms.
Journal ArticleDOI
Background-subtraction using contour-based fusion of thermal and visible imagery
James W. Davis,Vinay Sharma +1 more
TL;DR: A new background-subtraction technique fusing contours from thermal and visible imagery for persistent object detection in urban settings is presented, evaluated quantitatively and compared with other low- and high-level fusion techniques using manually segmented data.
Journal ArticleDOI
Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising
TL;DR: This work introduces optimal inverses for the Anscombe transformation, in particular the exact unbiased inverse, a maximum likelihood (ML) inverse, and a more sophisticated minimum mean square error (MMSE) inverse.
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