Publication

200 km Fiber-Loop Conventional Brillouin Distributed Sensor with 2m Spatial Resolution Using Image Denoising

Luc Thévenaz
2016
Conference Papers

Abstract

Non-local means image denoising method is optimized to boost the performance of Brillouin distributed fiber sensors. Results demonstrate ultra-long sensing over a 200-km fiber-loop with 2m resolution using a sensing scheme without any hardware sophistication.

Official source

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Luc Thévenaz
2016
Conference Papers

Abstract

Official source

Related concepts (24)

Total variation denoising

In signal processing, particularly , total variation denoising, also known as total variation regularization or total variation filtering, is a noise removal process (filter). It is based on the principle that signals with excessive and possibly spurious detail have high total variation, that is, the integral of the absolute is high. According to this principle, reducing the total variation of the signal—subject to it being a close match to the original signal—removes unwanted detail whilst preserving important details such as .

Image resolution

Image resolution is the level of detail an holds. The term applies to digital images, film images, and other types of images. "Higher resolution" means more image detail. Image resolution can be measured in various ways. Resolution quantifies how close lines can be to each other and still be visibly resolved. Resolution units can be tied to physical sizes (e.g. lines per mm, lines per inch), to the overall size of a picture (lines per picture height, also known simply as lines, TV lines, or TVL), or to angular subtense.

Non-local means

Non-local means is an algorithm in image processing for . Unlike "local mean" filters, which take the mean value of a group of pixels surrounding a target pixel to smooth the image, non-local means filtering takes a mean of all pixels in the image, weighted by how similar these pixels are to the target pixel. This results in much greater post-filtering clarity, and less loss of detail in the image compared with local mean algorithms. If compared with other well-known denoising techniques, non-local means adds "method noise" (i.

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