[问答]

快速双边滤波算法请教

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问答对人有帮助，内容完整，我也想知道答案 0 % % output = bilateralFilter( data, edge, ... % edgeMin, edgeMax, ... % sigmaSpatial, sigmaRange, ... % samplingSpatial, samplingRange ) % % Bilateral and Cross-Bilateral Filter using the Bilateral Grid. % % Bilaterally filters the image 'data' using the edges in the image 'edge'. % If 'data' == 'edge', then it the standard bilateral filter. % Otherwise, it is the 'cross' or 'joint' bilateral filter. % For convenience, you can also pass in [] for 'edge' for the normal % bilateral filter. % % Note that for the cross bilateral filter, data does not need to be % defined everywhere. Undefined values can be set to 'NaN'. However, edge % does need to be defined everywhere. % % data and edge should be of the greyscale, double-precision floating point % matrices of the same size (i.e. they should be [ height x width ]) % % data is the only required argument % % edgeMin and edgeMax specifies the min and max values of 'edge' (or 'data' % for the normal bilateral filter) and is useful when the input is in a % range that's not between 0 and 1. For instance, if you are filtering the % L channel of an image that ranges between 0 and 100, set edgeMin to 0 and % edgeMax to 100. % % edgeMin defaults to min( edge( : ) ) and edgeMax defaults to max( edge( : ) ). % This is probably not what you want, since the input may not span the % entire range. % % sigmaSpatial and sigmaRange specifies the standard deviation of the space % and range gaussians, respectively. % sigmaSpatial defaults to min( width, height ) / 16 % sigmaRange defaults to ( edgeMax - edgeMin ) / 10. % % samplingSpatial and samplingRange specifies the amount of downsampling % used for the approximation. Higher values use less memory but are also % less accurate. The default and recommended values are: % % samplingSpatial = sigmaSpatial % samplingRange = sigmaRange % function output = fBilateralFilter_ReviseVer( data, edge, edgeMin, edgeMax, sigmaSpatial, sigmaRange, ... samplingSpatial, samplingRange ) if( ndims( data ) > 2 ), error( 'data must be a greyscale image with size [ height, width ]' ); end if( ~isa( data, 'double' ) ), error( 'data must be of class "double"' ); end if ~exist( 'edge', 'var' ), edge = data; elseif isempty( edge ), edge = data; end if( ndims( edge ) > 2 ), error( 'edge must be a greyscale image with size [ height, width ]' ); end if( ~isa( edge, 'double' ) ), error( 'edge must be of class "double"' ); end inputHeight = size( data, 1 ); inputWidth = size( data, 2 ); if ~exist( 'edgeMin', 'var' ), edgeMin = min( edge( : ) ); warning( 'edgeMin not set! Defaulting to: %fn', edgeMin ); end if ~exist( 'edgeMax', 'var' ), edgeMax = max( edge( : ) ); warning( 'edgeMax not set! Defaulting to: %fn', edgeMax ); end edgeDelta = edgeMax - edgeMin;% hl- span of range % hl- assign scale parameters in both spatial and range domain if ~exist( 'sigmaSpatial', 'var' ), sigmaSpatial = min( inputWidth, inputHeight ) / 16; fprintf( 'Using default sigmaSpatial of: %fn', sigmaSpatial ); end if ~exist( 'sigmaRange', 'var' ), sigmaRange = 0.1 * edgeDelta; fprintf( 'Using default sigmaRange of: %fn', sigmaRange ); end if ~exist( 'samplingSpatial', 'var' ), samplingSpatial = sigmaSpatial; end if ~exist( 'samplingRange', 'var' ), samplingRange = sigmaRange; end if size( data ) ~= size( edge ), error( 'data and edge must be of the same size' ); end % parameters derivedSigmaSpatial = sigmaSpatial / samplingSpatial; % ?????????? derivedSigmaRange = sigmaRange / samplingRange; paddingXY = floor( 2 * derivedSigmaSpatial ) + 1; paddingZ = floor( 2 * derivedSigmaRange ) + 1; % allocate 3D grid downsampledWidth = floor( ( inputWidth - 1 ) / samplingSpatial ) + 1 + 2 * paddingXY; % paddingXY - 控制延拓范围 downsampledHeight = floor( ( inputHeight - 1 ) / samplingSpatial ) + 1 + 2 * paddingXY; downsampledDepth = floor( edgeDelta / samplingRange ) + 1 + 2 * paddingZ; gridData = zeros( downsampledHeight, downsampledWidth, downsampledDepth ); gridWeights = zeros( downsampledHeight, downsampledWidth, downsampledDepth ); % compute downsampled indices [ jj, ii ] = meshgrid( 0 : inputWidth - 1, 0 : inputHeight - 1 ); % hl- create the coordinats of xy-plane; jj - y coordinates of all pixels, ii - x coordinates of all pixels % ii = % 0 0 0 0 0 % 1 1 1 1 1 % 2 2 2 2 2 % jj = % 0 1 2 3 4 % 0 1 2 3 4 % 0 1 2 3 4 % so when iterating over ii( k ), jj( k ) % get: ( 0, 0 ), ( 1, 0 ), ( 2, 0 ), ... (down columns first) %% Compute the downsampled coordinates di = round( ii / samplingSpatial ) + paddingXY + 1; % round: Round to nearest integer四舍五入 dj = round( jj / samplingSpatial ) + paddingXY + 1; dz = round( ( edge - edgeMin ) / samplingRange ) + paddingZ + 1; %% hl - average sampling (box sampling) % perform scatter (there's probably a faster way than this) % normally would do downsampledWeights( di, dj, dk ) = 1, but we have to % perform a summation to do box downsampling for k = 1 : numel( dz ), % numel: Number of elements in an array dataZ = data( k ); % traverses the image column wise, same as di( k ) if ~isnan( dataZ ), dik = di( k ); %取出坐标 djk = dj( k ); dzk = dz( k ); gridData( dik, djk, dzk ) = gridData( dik, djk, dzk ) + dataZ; gridWeights( dik, djk, dzk ) = gridWeights( dik, djk, dzk ) + 1; end end % make gaussian kernel kernelWidth = 2 * derivedSigmaSpatial + 1; kernelHeight = kernelWidth; kernelDepth = 2 * derivedSigmaRange + 1; halfKernelWidth = floor( kernelWidth / 2 ); halfKernelHeight = floor( kernelHeight / 2 ); halfKernelDepth = floor( kernelDepth / 2 ); [gridX, gridY, gridZ] = meshgrid( 0 : kernelWidth - 1, 0 : kernelHeight - 1, 0 : kernelDepth - 1 ); gridX = gridX - halfKernelWidth; gridY = gridY - halfKernelHeight; gridZ = gridZ - halfKernelDepth; gridRSquared = ( gridX .* gridX + gridY .* gridY ) / ( derivedSigmaSpatial * derivedSigmaSpatial ) + ( gridZ .* gridZ ) / ( derivedSigmaRange * derivedSigmaRange ); kernel = exp( -0.5 * gridRSquared ); % convolve blurredGridData = convn( gridData, kernel, 'same' ); blurredGridWeights = convn( gridWeights, kernel, 'same' ); % divide blurredGridWeights( blurredGridWeights == 0 ) = -2; % avoid divide by 0, won't read there anyway normalizedBlurredGrid = blurredGridData ./ blurredGridWeights; normalizedBlurredGrid( blurredGridWeights < -1 ) = 0; % put 0s where it's undefined % for debugging % blurredGridWeights( blurredGridWeights < -1 ) = 0; % put zeros back % upsample [ jj, ii ] = meshgrid( 0 : inputWidth - 1, 0 : inputHeight - 1 ); % meshgrid does x, then y, so output arguments need to be reversed % no rounding di = ( ii / samplingSpatial ) + paddingXY + 1; dj = ( jj / samplingSpatial ) + paddingXY + 1; dz = ( edge - edgeMin ) / samplingRange + paddingZ + 1; % for p=1:inputHeight % for q=1:inputWidth % A{p,q}=[num2str(di(p,q)),' ',num2str(dj(p,q)),' ',num2str(dz(p,q))]; % end; % end; % interpn takes rows, then cols, etc % i.e. size(v,1), then size(v,2), ... output = interpn( normalizedBlurredGrid, di, dj, dz ); % N-D data interpolation 复制代码其中第117，118，119行中的downsampledWidth，downsampledHeight，downsampledDepth为什么要加1？ 0
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