نتایج جستجو برای: non-local means
تعداد نتایج: 2070189 فیلتر نتایج به سال:
among abundant image denoising methods proposed so far, the use of patchbased algorithms have attracted a lot of attention from image processingcommunity. although these methods are very powerful in presentation of highquality results, the impact of human visual system (hvs) is ignored in sole of them.in this paper the human visual geometry is used in preparation of a new method forimage denois...
Non-local means is a recently proposed denoising technique that better preserves image structures than other methods. However, the computational cost of non-local means is prohibitive, especially for large 3D images. Modifications have previously been proposed to reduce the cost, which result in image artefacts. This paper proposes a compact rotation invariant descriptor. Testing demonstrates i...
We present in this paper a new denoising method called non-local means. The method is based on a simple principle: replacing the color of a pixel with an average of the colors of similar pixels. But the most similar pixels to a given pixel have no reason to be close at all. It is therefore licit to scan a vast portion of the image in search of all the pixels that really resemble the pixel one w...
In Non-Local Means (NLM), each pixel is denoised by performing a weighted averaging of its neighboring pixels, where the weights are computed using image patches. We demonstrate that the denoising performance of NLM can be improved by pruning the neighboring pixels, namely, by rejecting neighboring pixels whose weights are below a certain threshold λ. While pruning can potentially reduce pixel ...
Today, with the advancement of technology, the use of tools for extracting information from video are much wider in terms of both visual power and the processing power. High-speed car, perfect detection accuracy, business diversity in the fields of medical, home appliances, smart cars, humanoid robots, military systems and the commercialization makes these systems cost effective. Among the most...
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