Remote Sensing Image Denoising Using Patch Grouping-Based Nonlocal Means Algorithm

Xu, Su Xiu; Zhou, Yingyue; Xiang, Hongbing; Li, Shuiying

doi:10.1109/lgrs.2017.2761812

Cited by 13 publications

(11 citation statements)

References 22 publications

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“…The labels a 1 -e 1 denotes the actual clean UAV images, the a 2 -e 2 denotes the synthetic noise image, and the a 3 –e 3 denotes the UAV image denoised by method [ 8 ]. The label a 4 –e 4 denotes the denoised UAV image by method [ 5 ], the a 5 –e 5 identifies the UAV image denoised by method [ 17 ], and the a 6 –e 6 denotes the UAV image denoised by the proposed method. In each of the images, small rectangles in white and blue identify areas enlarged for examination in the larger white and blue rectangles in each series of images with different levels of noise.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 4 and Figure 5 shows the matching results by SIFT. In Figure 4 , when the noise level is 35, the labels b 1 –e 1 indicates the SIFT matching result of the original clean image and the denoised image obtained by method [ 8 ], the labels b 2 –e 2 indicates the SIFT matching result of the original clean image and the denoised image obtained by method [ 5 ], the labels b 3 –e 3 indicates the SIFT matching result of the original clean image and the denoised image obtained by method [ 17 ], the labels b 4 –e 4 indicates the SIFT matching result of the original clean image and the denoised image obtained by the proposed method. Figure 5 is similar to Figure 4 , but shows results at noise level 55.…”

Section: Methodsmentioning

confidence: 99%

“…In Table 3 , Column 1 denotes five randomly picked testing images, Column 2 denotes correct matching pairs of the original clean image and the denoised image obtained by method [ 8 ], Column 3 denotes correct matching pairs of the original clean image and the denoised image obtained by method [ 5 ], Column 4 denotes correct matching pairs of the original clean image and the denoised image obtained by method [ 17 ], Column 5 denotes correct matching pairs of the original clean image and the denoised image obtained by proposed method.…”

Section: Methodsmentioning

confidence: 99%

“…Wang et al [ 4 ] proposed high-order balanced multi-band multiwavelet packet transforms to denoise remote sensing images and claimed that an appropriate band number can improve the denoising performance. Xu et al [ 5 ] provided a method based on blockwise nonlocal means algorithm to denoise repetitive image patches in remote sensing images. Penna et al [ 6 ] utilized non local means with the stochastic distances to denoise SAR images.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks

Wang

Xiao

Guo

et al. 2018

Sensors

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Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep neural network method based on generative adversarial learning to trace the mapping relationship between noisy and clean images. In our approach, perceptual reconstruction loss is used to establish a loss equation that continuously optimizes a min-max game theoretic model to obtain better UAV image denoising results. The generated denoised images by the proposed method enjoy clearer ground objects edges and more detailed textures of ground objects. In addition to the traditional comparison method, denoised UAV images and corresponding original clean UAV images were employed to perform image matching based on local features. At the same time, the classification experiment on the denoised images was also conducted to compare the denoising results of UAV images with others. The proposed method had achieved better results in these comparison experiments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks

Wang

Xiao

Guo

et al. 2018

Sensors

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show abstract

“…Neighbor embedding methods find the nearest neighbors in the observed image Y, and reconstruct image X by computing the high-resolution embedding using the appropriate high-resolution features of the K nearest neighbors [3][4][5]. Xu et al [23] uses patch grouping-based nonlocal means algorithm to denoise remote sensing images. C. Kwan and J. Zhou [24] propose a patent for image denoising, which provided a useful method for practice.…”

Section: Traditional Algorithmsmentioning

confidence: 99%

Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

Wang

et al. 2018

Remote Sensing

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The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB.

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Supervised band selection in hyperspectral images using single-layer neural networks

Habermann

Frémont

Shiguemori

2018

International Journal of Remote Sensing

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Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.

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Remote Sensing Image Denoising Using Patch Grouping-Based Nonlocal Means Algorithm

Cited by 13 publications

References 22 publications

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks

Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

Supervised band selection in hyperspectral images using single-layer neural networks

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