各种图像去噪代码总结（matlab）（10种）

function output = bilateralFilter( data, edge, sigmaSpatial, sigmaRange, ... samplingSpatial, samplingRange ) if ~exist( 'edge', 'var' ), edge = data; end inputHeight = size( data, 1 ); inputWidth = size( data, 2 ); if ~exist( 'sigmaSpatial', 'var' ), sigmaSpatial = min( inputWidth, inputHeight ) / 16; end edgeMin = min( edge( : ) ); edgeMax = max( edge( : ) ); edgeDelta = edgeMax - edgeMin; if ~exist( 'sigmaRange', 'var' ), sigmaRange = 0.1 * edgeDelta; 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; 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 ); di = round( ii / samplingSpatial ) + paddingXY + 1; dj = round( jj / samplingSpatial ) + paddingXY + 1; dz = round( ( edge - edgeMin ) / samplingRange ) + paddingZ + 1; % 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 ), dataZ = data( k ); % traverses the image column wise, same as di( k ) if ~isnan( dataZ ), dik = di( k ); 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 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; % interpn takes rows, then cols, etc % i.e. size(v,1), then size(v,2), ... output = interpn( normalizedBlurredGrid, di, dj, dz );