- %
- % 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?
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