Removing Poisson Noise by Optimization of Weights in Non-Local Means

Jin, Qiyu; Grama, Ion; Liu, Quansheng

doi:10.1109/sopo.2012.6270436

Cited by 9 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4c). To further reduce noise but to preserve edges in the mosaic images, we applied a nonlocal mean filter (Jin et al, 2011;Condat, 2010). The filtered image is shown in Fig.…”

Section: Multiorbital Composite Image For Switzerlandmentioning

confidence: 99%

“…In the median filtered difference image, from all pixels brighter than a threshold of 4 dB, the brightest 5 % were considered the mask of potential avalanches. The threshold was determined empirically based on TSX data, but other authors also used thresholds of 4-6 dB (Eckerstorfer et al, 2019;Karbou et al, 2018;Vickers et al, 2016). To remove isolated bright pixels from the mask, we determined around each continuous area an ellipse and removed areas with a major axis shorter than 75 m (for both TSX and S1).…”

Section: Automated Avalanche Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Leinß

Wicki

Holenstein

et al. 2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m2 for TSX and 2000 m2 for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018.

show abstract

“…4c). To further reduce noise but to preserve edges in the mosaic images, we applied a nonlocal mean filter (Jin et al, 2011;Condat, 2010). The filtered image is shown in Fig.…”

Section: Multiorbital Composite Image For Switzerlandmentioning

confidence: 99%

Section: Automated Avalanche Detectionmentioning

confidence: 99%

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Leinß

Wicki

Holenstein

et al. 2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…4d). To further reduce noise but to preserve edges in the mosaic images, we applied a non-local mean filter (Jin et al, 2011;Condat, 2010). The filtered image is shown in Fig.…”

Section: Multiorbital Composite Image For Switzerlandmentioning

confidence: 99%

Snow Avalanche Detection and Mapping in single, multitemporal, and multiorbital Radar Images from TerraSAR-X and Sentinel-1

Leinß¹,

Wicki²,

Holenstein³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanches reports from a network of observers. During bad weather, however, information about occurred avalanches can be scarce or even be missing completely. To asses the potential of weather independent radar satellites we compared manual and automatic avalanche mapping results from high resolution TerraSAR-X (TSX) stripmap images and from medium resolution Sentinel-1 (S1) interferometric wide swath images. Within a selected test site in the central Swiss Alps the TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other but with TSX about 40 % more, mainly smaller avalanches were detected. However, S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 days with freely available acquisitions. With costly SPOT-6 images the Alps can be even covered in a single day at meter-resolution, at least for clear sky conditions. For the SPOT-6 and TSX mapping results we found a fair agreement but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, with radar, mainly the avalanche deposition zone was detected, whereas the release zone was well visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of the manually mapped new avalanches in the same image pair, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single orbit TSX change detection results. In a multiorbital S1 moasic covering entire Switzerland, we detected 7361 new avalanches which occurred during an extreme avalanche period around Jan 4th 2018.

show abstract

“…Nevertheless, the increase in speed of the SIL algorithm compared to the basic one never exceeds the factor 4.5. However it should be noted that the speed increase may be much more important for NLM variants where the recommended patch size is greater than the maximum value of d s = 4 obtained here, see for example [14]. In the experiments the NLM-B algorithm appears much slower than NLM-P/NLM-Pa, with a CPU-time ratio between 6 (Color IPOL database, σ = 25) and 49 (Gray-level IPOL database, σ = 83).…”

Section: 52)mentioning

confidence: 76%

Parameter-Free Fast Pixelwise Non-Local Means Denoising

Froment¹

2014

Image Processing on Line

View full text Add to dashboard Cite

This article proposes a fast and open-source implementation of the well-known Non-Local Means (NLM) denoising algorithm, in its original pixelwise formulation. The fast implementation is based on the computation of patch distances using sums of lines that are invariant under a patch shift. The optimal parameters of NLM (in the average peak signal to noise ratio -PSNR -sense) are computed from an image database, thereby leading to a parameter-free NLM implementation. Comparison is performed with the parameter-free blockwise NLM implementation already proposed in IPOL journal by Buades, Coll and Morel. As expected the blockwise implementation offers better PSNR, at least when the noise standard deviation is large enough, but there is no significant difference in quality when performing visual inspection. The highlight is that the proposed parameter-free pixelwise NLM implementation is faster than the patchwise one by a factor of 6 to 49. Source CodeThe reviewed source code and documentation for the parameter-free fast pixelwise NLM algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.

show abstract

Removing Poisson Noise by Optimization of Weights in Non-Local Means

Cited by 9 publications

References 33 publications

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Snow Avalanche Detection and Mapping in single, multitemporal, and multiorbital Radar Images from TerraSAR-X and Sentinel-1

Parameter-Free Fast Pixelwise Non-Local Means Denoising

Contact Info

Product

Resources

About