www.gusucode.com > matlab编程NSCT分解 图像融合 各个融合指标评价体系 分解源码程序 > matlab编程NSCT分解 图像融合 各个融合指标评价体系 分解源码程序/NSCT/nsdfbdec.m
function y = nsdfbdec( x, dfilter, clevels ) % NSDFBDEC Nonsubsampled directional filter bank decomposition. % NSDFBDEC Decompose the image X by a nonsubsampled directional filter bank % with a binary-tree structure. It outputs the final branches, totally 2^clevels. % There is no subsampling and hence the operation is shift-invariant. % % nsdfbdec( x, dfilter, [clevels] ) % % INPUT: % x: % an array, input image. % dfilter: % a string, directional filter name. % a cell of matrices, including two directional filters and eight % parallelogram filters. % clevels: % a non-negative integer, number of decomposition levels. % % OUTPUT: % y: % a cell vector, output subbands. % % See also: DFILTERS, PARAFILTERS, NSSFBDEC. % % History: % 08/06/2004 Created by Jianping Zhou. % Input check if ~exist('clevels', 'var') clevels = 0 ; y{1} = x; return; end if (clevels ~= round(clevels)) | (clevels < 0) error('Number of decomposition levels must be a non-negative integer'); end if clevels == 0 % No decomposition, simply copy input to output y{1} = x; return; end if ~ischar( dfilter ) if iscell( dfilter ) if length( dfilter ) ~= 4 error('You shall provide a cell of two 2D directional filters and two groups of 2D parallelogram filters!'); end else error('You shall provide the name of directional filter or all filters!'); end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Get fan filters, parallelogram filters, and basic sampling matrices %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Get the diamond filters, if necessary if ischar( dfilter ) % Get the directional filters for the critically sampled DFB. [h1, h2] = dfilters(dfilter, 'd'); % A scale is required for the nonsubsampled case. h1 = h1./sqrt(2) ; h2 = h2./sqrt(2) ; % Generate the first-level fan filters by modulations. k1 = modulate2(h1, 'c'); k2 = modulate2(h2, 'c'); % Obtain the parallelogram filters from the diamond filters [f1, f2] = parafilters( h1, h2 ) ; else % Copy the fan filters directly. k1 = dfilter{1} ; k2 = dfilter{2} ; % Copy the parallelogram filters directly. f1 = dfilter{3} ; f2 = dfilter{4} ; end % Quincunx sampling matrices q1 = [1, -1; 1, 1]; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % First-level Decompositions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if clevels == 1 % No upsampling for filters at the first-level. [y{1}, y{2}] = nssfbdec( x, k1, k2 ) ; else %Others %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Second-level Decompositions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % No upsampling at filters for the first-level. [x1, x2] = nssfbdec( x, k1, k2 ) ; % Convolution with upsampled filters [y{1}, y{2}] = nssfbdec( x1, k1, k2, q1 ) ; [y{3}, y{4}] = nssfbdec( x2, k1, k2, q1 ) ; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Third and higher levels Decompositions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Third and higher levels decompositions for l = 3:clevels % Allocate space for the new subband outputs y_old = y; y = cell(1, 2^l); % The first half channels: for k = 1:2^(l-2) % Compute the upsampling matrix by the formula (3.18) of Minh N. Do's % thesis. The upsampling matrix for the channel k in a l-levels DFB is % M_k^{(l-1)} (refer to (3.18), pp. 53, Minh N. Do's thesis) % Compute s_{(l-1)}(k): slk = 2*floor( (k-1) /2 ) - 2^(l-3) + 1 ; % Compute the sampling matrix: mkl = 2*[ 2^(l-3), 0; 0, 1 ]*[1, 0; -slk, 1]; i = mod(k-1, 2) + 1; % Decompose by the two-channel filter bank: [y{2*k-1}, y{2*k}] = nssfbdec( y_old{k}, f1{i}, f2{i}, mkl ); end % The second half channels: for k = 2^(l-2)+1 : 2^(l-1) % Compute the upsampling matrix by the extension of the formula (3.18) % of Minh N. Do's thesis to the second half channels. % thesis. The upsampling matrix for the channel k in a l-levels DFB is % M_k^{(l-1)} (refer to notes by Jianping Zhou) % Compute s_{(l-1)}(k): slk = 2 * floor( ( k-2^(l-2)-1 ) / 2 ) - 2^(l-3) + 1 ; % Compute the sampling matrix: mkl = 2*[ 1, 0; 0, 2^(l-3) ]*[1, -slk; 0, 1]; i = mod(k-1, 2) + 3; % Decompose by the two-channel filter bank: [y{2*k-1}, y{2*k}] = nssfbdec( y_old{k}, f1{i}, f2{i}, mkl ); end end end