Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

Shen, Huanfeng; Zhao, Wennan; Yuan, Qiangqiang; Zhang, Liangpei

doi:10.3390/rs6087491

Cited by 17 publications

(9 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regardless of which resampler is used, they are all expected to provide results with improved geometric fidelity if the greater density of Sentinel-2 observations in the L1C tile overlap region is resampled. Similarly, image restoration approaches that use knowledge of the system Point Spread Function (Shen et al 2014) and pansharpening approaches that fuse higher spatial resolution panchromatic with lower spatial resolution multispectral imagery ) may benefit.…”

Section: Resultsmentioning

confidence: 99%

Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data

Roy

Zhang

et al. 2016

Remote Sensing Letters

View full text Add to dashboard Cite

The standard geolocated Sentinel-2 Multi Spectral Instrument (MSI) L1C data products are defined in spatially overlapping tiles in different Universal Transverse Mercator (UTM) map projection zones. Best practices for reprojection and resampling to properly utilize and benefit from the L1C data format are presented. Three sets of 10 m Sentinel-2 L1C data acquired in the same orbit at different latitudes are examined to illustrate and quantify (a) the spatial properties of the L1C data and provide insights into the occurrence of overlapping tiles and overlapping tiles defined in different UTM zones from the same MSI swath, (b) the geometric implications of resampling and reprojection approaches that consider only the data from one L1C tile and not the data from other tiles in the overlap region that are defined in different UTM zones and (c) a recommended approach that considers all the overlapping L1C tile data and is shown statistically and qualitatively to improve the geometric fidelity of the reprojected resampled L1C data. ARTICLE HISTORY

show abstract

Section: Resultsmentioning

confidence: 99%

Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data

Roy

Zhang

et al. 2016

Remote Sensing Letters

View full text Add to dashboard Cite

show abstract

“…The experiments were performed using a personal computer with a CPU speed of 3.70 GHz and a 64GByte RAM. In addition, we empirically set the regularization parameters to compute (13)- (15) to α = 10 5 , β = 8, and λ = 8, respectively. The proposed PSF estimation method uses the method described in subsection II-D. On the other hand, other PSF estimation methods uses their own method.…”

Section: Resultsmentioning

confidence: 99%

“…Another approach is to locate a sharp edge, which is considered as a blurred version of the ideal step edge, in the observed image. Shen et al proposed an edge detection method to locate and estimate the PSF [15]. More specifically, they detected knife-edges using the alternating minimization framework and then estimated parameters of the PSF.…”

Section: Introductionmentioning

confidence: 99%

Texture-Aware Deblurring for Remote Sensing Images Using $ \ell _0$-Based Deblurring and $ \ell _2$-Based Fusion

Lim

Park³

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

This paper presents an image deblurring method using 0-norm based deblurring and 2-norm based textureaware image fusion for remote sensing images. To restore the details of blurred texture, the proposed method first perform texture restoration by fusing the restored results using Richardson-Lucy deconvolution and unsharp masking. Next, we analyzed the intensity and dark channel properties of remote sensing images and perform the 0-norm based deblurring using the intensity and dark channel priors. Although the 0-norm based deblurring can provide a significantly restored result, it cannot overcome the loss of the texture region. On the other hand, the proposed 2norm based image fusion method can preserve both sharp edges and texture details. In the experiments, we demonstrate that the proposed method can provide better restored results than existing state-of-the-art deblurring methods without over-smoothing and undesired artifact.

show abstract

“…Image restoration has been widely studied in remote sensing image processing in the last decades. [1][2][3][4][5][6] Image restoration problem refers to recovering an image from blurry and noisy observation. For simplicity, we assume that the underlying images have square domains and are grayscale.…”

Section: Introductionmentioning

confidence: 99%

Restoration of remote sensing images based on nonconvex constrained high-order total variation regularization

Zhu

Hao

2019

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Convex total variation (TV) regularization models have been widely used in remote sensing image restoration problems; however, these models tend to produce staircase effects. We consider a nonconvex second-order TV regularization model with linear constraints for remote sensing image restoration. To solve the nonconvex second-order TV regularization model, we propose an efficient alternating minimization algorithm based on generalized iterated shrinkage algorithm and alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed model, which can reduce staircase effects while preserving edges. In terms of signal-to-noise ratio and structural similarity index measure, the experimental results show that our proposed model and algorithm can give better performance compared with some state-of-the-art methods.

show abstract

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

Cited by 17 publications

References 50 publications

Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data

Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data

Texture-Aware Deblurring for Remote Sensing Images Using $ \ell _0$-Based Deblurring and $ \ell _2$-Based Fusion

Restoration of remote sensing images based on nonconvex constrained high-order total variation regularization

Contact Info

Product

Resources

About