Mercurial > hg > smallbox
comparison util/sparco utils/genPDF.m @ 77:62f20b91d870
add routines from sparco problems privite folder to {root}\util
some changes to ksvd vs rlsdla image denoising example
author | Ivan <ivan.damnjanovic@eecs.qmul.ac.uk> |
---|---|
date | Fri, 25 Mar 2011 14:01:50 +0000 |
parents | |
children |
comparison
equal
deleted
inserted
replaced
76:d052ec5b742f | 77:62f20b91d870 |
---|---|
1 function [pdf,val] = genPDF(imSize,p,pctg,distType,radius,disp) | |
2 | |
3 %[pdf,val] = genPDF(imSize,p,pctg [,distType,radius,disp]) | |
4 % | |
5 % generates a pdf for a 1d or 2d random sampling pattern | |
6 % with polynomial variable density sampling | |
7 % | |
8 % Input: | |
9 % imSize - size of matrix or vector | |
10 % p - power of polynomial | |
11 % pctg - partial sampling factor e.g. 0.5 for half | |
12 % distType - 1 or 2 for L1 or L2 distance measure | |
13 % radius - radius of fully sampled center | |
14 % disp - display output | |
15 % | |
16 % Output: | |
17 % pdf - the pdf | |
18 % val - min sampling density | |
19 % | |
20 % | |
21 % Example: | |
22 % [pdf,val] = genPDF([128,128],2,0.5,2,0,1); | |
23 % | |
24 % (c) Michael Lustig 2007 | |
25 | |
26 % This file is used with the kind permission of Michael Lustig | |
27 % (mlustig@stanford.edu), and originally appeared in the | |
28 % SparseMRI toolbox, http://www.stanford.edu/~mlustig/ . | |
29 % | |
30 % $Id: genPDF.m 1040 2008-06-26 20:29:02Z ewout78 $ | |
31 | |
32 | |
33 if nargin < 4 | |
34 distType = 2; | |
35 end | |
36 | |
37 if nargin < 5 | |
38 radius = 0; | |
39 end | |
40 | |
41 if nargin < 6 | |
42 disp = 0; | |
43 end | |
44 | |
45 | |
46 minval=0; | |
47 maxval=1; | |
48 val = 0.5; | |
49 | |
50 if length(imSize)==1 | |
51 imSize = [imSize,1]; | |
52 end | |
53 | |
54 sx = imSize(1); | |
55 sy = imSize(2); | |
56 PCTG = floor(pctg*sx*sy); | |
57 | |
58 | |
59 if sum(imSize==1)==0 % 2D | |
60 [x,y] = meshgrid(linspace(-1,1,sy),linspace(-1,1,sx)); | |
61 switch distType | |
62 case 1 | |
63 r = max(abs(x),abs(y)); | |
64 otherwise | |
65 r = sqrt(x.^2+y.^2); | |
66 r = r/max(abs(r(:))); | |
67 end | |
68 | |
69 else %1d | |
70 r = abs(linspace(-1,1,max(sx,sy))); | |
71 end | |
72 | |
73 | |
74 | |
75 | |
76 idx = find(r<radius); | |
77 | |
78 pdf = (1-r).^p; pdf(idx) = 1; | |
79 if floor(sum(pdf(:))) > PCTG | |
80 error('infeasible without undersampling dc, increase p'); | |
81 end | |
82 | |
83 % begin bisection | |
84 while(1) | |
85 val = minval/2 + maxval/2; | |
86 pdf = (1-r).^p + val; pdf(find(pdf>1)) = 1; pdf(idx)=1; | |
87 N = floor(sum(pdf(:))); | |
88 if N > PCTG % infeasible | |
89 maxval=val; | |
90 end | |
91 if N < PCTG % feasible, but not optimal | |
92 minval=val; | |
93 end | |
94 if N==PCTG % optimal | |
95 break; | |
96 end | |
97 end | |
98 | |
99 if disp | |
100 figure, | |
101 subplot(211), imshow(pdf) | |
102 if sum(imSize==1)==0 | |
103 subplot(212), plot(pdf(end/2+1,:)); | |
104 else | |
105 subplot(212), plot(pdf); | |
106 end | |
107 end | |
108 | |
109 | |
110 | |
111 | |
112 | |
113 |