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/*M///////////////////////////////////////////////////////////////////////////////////////
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// copy or use the software.
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// For Open Source Computer Vision Library
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
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#ifndef __OPENCV_PHOTO_CUDA_HPP__
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#define __OPENCV_PHOTO_CUDA_HPP__
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#include "opencv2/core/cuda.hpp"
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namespace cv { namespace cuda {
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//! @addtogroup photo_denoise
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/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast.
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@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
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@param dst Destination image.
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@param h Filter sigma regulating filter strength for color.
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@param search_window Size of search window.
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@param block_size Size of block used for computing weights.
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@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
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BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
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@param stream Stream for the asynchronous version.
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CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst,
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int search_window = 21,
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int borderMode = BORDER_DEFAULT,
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Stream& stream = Stream::Null());
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/** @brief Perform image denoising using Non-local Means Denoising algorithm
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<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising> with several computational
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optimizations. Noise expected to be a gaussian white noise
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@param src Input 8-bit 1-channel, 2-channel or 3-channel image.
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@param dst Output image with the same size and type as src .
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@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
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removes image details, smaller h value preserves details but also preserves some noise
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@param search_window Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater search_window - greater
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denoising time. Recommended value 21 pixels
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@param block_size Size in pixels of the template patch that is used to compute weights. Should be
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odd. Recommended value 7 pixels
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@param stream Stream for the asynchronous invocations.
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This function expected to be applied to grayscale images. For colored images look at
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FastNonLocalMeansDenoising::labMethod.
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CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst,
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int search_window = 21,
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Stream& stream = Stream::Null());
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/** @brief Modification of fastNlMeansDenoising function for colored images
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@param src Input 8-bit 3-channel image.
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@param dst Output image with the same size and type as src .
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@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
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also removes image details, smaller h value preserves details but also preserves some noise
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@param photo_render float The same as h but for color components. For most images value equals 10 will be
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enough to remove colored noise and do not distort colors
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@param search_window Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater search_window - greater
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denoising time. Recommended value 21 pixels
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@param block_size Size in pixels of the template patch that is used to compute weights. Should be
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odd. Recommended value 7 pixels
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@param stream Stream for the asynchronous invocations.
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The function converts image to CIELAB colorspace and then separately denoise L and AB components
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with given h parameters using FastNonLocalMeansDenoising::simpleMethod function.
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fastNlMeansDenoisingColored
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CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst,
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float h_luminance, float photo_render,
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int search_window = 21,
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Stream& stream = Stream::Null());
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}} // namespace cv { namespace cuda {
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#endif /* __OPENCV_PHOTO_CUDA_HPP__ */