-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathDTBDM.m
207 lines (203 loc) · 7.82 KB
/
DTBDM.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
close all;
x=imread('G:\project\imgpro2\Lena.bmp');
x =imresize(x,[512 512]);
NOISE_VAR = 0.2;
y=x;
%for z = 1:75
r = randi(255,1,2);
y=pepperOrSalt(y,NOISE_VAR, 3, r(1), r(2));
%end
y = double(y);
decision1=0;decision2=0;decision3=0;decision4=0;
f = y;f_m = y;
[R, C] = size(x);
th_ima=20;th_imb=25;th_fma=40;th_fmb=80;th_sma=15;th_smb=60;
for z =1:1
for i = 2:R-1
for j = 2:C-1
tmp =y(i-1:i+1,j-1:j+1);
w_top_half=[y(i-1,j-1),y(i-1,j),y(i-1,j+1),y(i,j-1)];
w_bottom_half=[y(i,j+1),y(i+1,j-1),y(i+1,j),y(i+1,j+1)];
s_top = sort(w_top_half);
s_bottom = sort(w_bottom_half);
s_top_max = s_top(4);
s_top_min = s_top(1);
s_bottom_min = s_bottom(1);
s_bottom_max = s_bottom(4);
top_half_diff = abs(s_top_max-s_top_min);
bottom_half_diff = abs(s_bottom_max-s_bottom_min);
if (top_half_diff>=th_ima || bottom_half_diff>=th_ima)
decision1=1;
else
decision1=0;
end
if(decision1==0)
if(abs(y(i,j)-s_top_max)>=th_imb || abs(y(i,j)-s_top_min)>=th_imb)
im_top_half=1;
else
im_top_half=0;
end
if(abs(y(i,j)-s_bottom_max)>=th_imb || abs(y(i,j)-s_bottom_min)>=th_imb)
im_bottom_half=1;
else
im_bottom_half=0;
end
if(im_top_half==1 || im_bottom_half==1)
decision2 =1;
else
decision2 =0;
end
%else
% y(i,j) = y(i,j);
end
%if(decision2==1)
if(abs(y(i-1,j-1)-y(i,j))>=th_fma || abs(y(i+1,j+1)-y(i,j))>=th_fma || abs(y(i-1,j-1)-y(i+1,j+1))>=th_fma)
fm_e1=0;%no edge
else
fm_e1=1;
end
if(abs(y(i-1,j+1)-y(i,j))>=th_fma || abs(y(i+1,j-1)-y(i,j))>=th_fma || abs(y(i-1,j+1)-y(i+1,j-1))>=th_fma)
fm_e2=0;
else
fm_e2=1;
end
if(abs(y(i-1,j)-y(i,j))>=th_fma || abs(y(i+1,j)-y(i,j))>=th_fma || abs(y(i-1,j)-y(i+1,j))>=th_fma)
fm_e3=0;
else
fm_e3=1;
end
if(abs(y(i,j-1)-y(i,j))>=th_fma || abs(y(i,j+1)-y(i,j))>=th_fma || abs(y(i,j-1)-y(i,j+1))>=th_fma)
fm_e4=0;
else
fm_e4=1;
end
if(fm_e1==1 || fm_e2==1 || fm_e3==1 || fm_e4==1)
decision3=0;
else
decision3=1;
end
%else
% y(i,j) = y(i,j);
%end
%if(decision3==1)
s_w=sort(tmp);
w4=s_w(4);
w_median=s_w(5);
w6=s_w(6);
w_max=w6+th_sma;
w_min=w4-th_sma;
if(w_max<=w_median+th_smb)
n_max=w_max;
else
n_max=w_median+th_smb;
end
if(w_min>=w_median-th_smb)
n_min=w_min;
else
n_min=w_median-th_smb;
end
if(y(i,j)>=n_max || y(i,j)<=n_min)
decision4=1;
else
decision4=0;
end
%else
% y(i,j) = y(i,j);
%end
if(((decision4==1)||(decision3==1)||((decision2==1)&&(decision1==0))))
d1=abs(y(i,j-1)-y(i+1,j+1))+abs(y(i-1,j-1)-y(i,j+1));
d2=abs(y(i-1,j-1)-y(i+1,j))+abs(y(i-1,j)-y(i+1,j+1));
d3=(abs(y(i-1,j)-y(i+1,j)))*2;
d4=abs(y(i-1,j)-y(i+1,j-1))+abs(y(i-1,j+1)-y(i+1,j));
d5=abs(y(i-1,j+1)-y(i,j-1))+abs(y(i,j+1)-y(i+1,j-1));
d6=(abs(y(i,j-1)-y(i,j+1)))*2;
d7=(abs(y(i-1,j-1)-y(i+1,j+1)))*2;
d8=(abs(y(i-1,j+1)-y(i+1,j-1)))*2;
if((y(i,j-1)>=w_max || y(i,j-1)<=w_min) && (y(i,j+1)>=w_max ||y(i,j+1)<=w_min) && (y(i+1,j-1)>=w_max || y(i+1,j-1)<=w_min )&& (y(i+1,j)>=w_max || y(i+1,j)<=w_min) && (y(i+1,j+1)>=w_max || y(i+1,j+1)<=w_min))
f(i,j)=(y(i-1,j-1)+(y(i-1,j)*2)+y(i-1,j+1))/4;
else
if(y(i,j-1)>=w_max || y(i,j-1)<=w_min)
d_m=[d2 d3 d4 d7 d8];
elseif(y(i,j+1)>=w_max ||y(i,j+1)<=w_min)
d_m=[d2 d3 d4 d7 d8];
elseif(y(i+1,j-1)>=w_max || y(i+1,j-1)<=w_min)
d_m=[d1 d2 d3 d6 d7];
elseif(y(i+1,j)>=w_max || y(i+1,j)<=w_min)
d_m=[d1 d5 d6 d7 d8];
elseif(y(i+1,j+1)>=w_max || y(i+1,j+1)<=w_min)
d_m=[d3 d4 d5 d6 d8];
else
d_m=[d1 d2 d3 d4 d5 d6 d7 d8];
end
s_d=sort(d_m);
d_min=s_d(1);
if(d_min==d1)
y(i,j)=(y(i-1,j-1)+y(i,j-1)+y(i,j+1)+y(i+1,j+1))/4;
elseif(d_min==d2)
y(i,j)=(y(i-1,j-1)+y(i-1,j)+y(i+1,j)+y(i+1,j+1))/4;
elseif(d_min==d3)
y(i,j)=(y(i-1,j)+y(i+1,j))/2;
elseif(d_min==d4)
y(i,j)=(y(i-1,j)+y(i-1,j+1)+y(i+1,j-1)+y(i+1,j))/4;
elseif(d_min==d5)
y(i,j)=(y(i-1,j+1)+y(i,j-1)+y(i,j+1)+y(i+1,j-1))/4;
elseif(d_min==d6)
y(i,j)=(y(i,j-1)+y(i,j+1))/2;
elseif(d_min==d7)
y(i,j)=(y(i-1,j-1)+y(i+1,j+1))/2;
elseif(d_min==d8)
y(i,j)=(y(i-1,j+1)+y(i+1,j-1))/2;
end
end
m=[y(i,j) y(i-1,j) y(i,j-1) y(i,j+1) y(i+1,j)];
y(i,j) = median(m);
%else
% y(i,j) = y(i,j);
end
%if(f_m(i,j)>=n_max||f_m(i,j)<=n_min)
% if((f_m(i-1,j-1)<n_max)&&(f_m(i-1,j-1)>n_min))
% f_m(i,j)=f_m(i-1,j-1);
% elseif((f_m(i-1,j)<n_max)&&(f_m(i-1,j)>n_min))
% f_m(i,j)=f_m(i-1,j);
%elseif((f_m(i-1,j+1)<n_max)&&(f_m(i-1,j+1)>n_min))
% f_m(i,j)=f_m(i-1,j+1);
%elseif((f_m(i,j-1)<n_max)&&(f_m(i,j-1)>n_min))
% f_m(i,j)=f_m(i,j-1);
%elseif((f_m(i,j+1)<n_max)&&(f_m(i,j+1)>n_min))
% f_m(i,j)=f_m(i,j+1);
%elseif((f_m(i+1,j-1)<n_max)&&(f_m(i+1,j-1)>n_min))
% f_m(i,j)=f_m(i+1,j-1);
%elseif((f_m(i+1,j)<n_max)&&(f_m(i+1,j)>n_min))
% f_m(i,j)=f_m(i+1,j);
%elseif((f_m(i+1,j+1)<n_max)&&(f_m(i+1,j+1)>n_min))
% f_m(i,j)=f_m(i+1,j+1);
%else
% f_m(i,j)=(f_m(i-1,j-1)+(f_m(i-1,j)*2)+f_m(i-1,j+1))/4;
%end
%end
end
end
end
figure(1);imshow(x);figure(2);imshow(f_m,[]);figure(3);imshow(y,[]);
squaredErrorImage = (double(x) - (y)) .^ 2;
mse = sum(sum(squaredErrorImage)) / (R * C);
PSNR = 10 * log10( 255^2 / mse);
r1=rand_index(y);
v=uint8(y);
u=uint8(f_m);
gce1 = global_consistancy_error(y);
jaccardIndex_ac = sum(x(:) & v(:)) / sum(x(:) | v(:));
message = sprintf('The mean square error for denoised image is %.2f.\nThe PSNR = %.2f.\nThe Rand_index = %.2f.\nThe global_consistancy_error = %.2f.\nThe jaccard coefficient = %.2f.\nThe jaccard distance = %.2f.', mse, PSNR, r1,gce1,jaccardIndex_ac,1-jaccardIndex_ac);
msgbox(message);
imwrite(v,'G:\project\imgpro2\img.bmp');
squaredErrorImage1 = (double(x) - (f_m)) .^ 2;
mse1 = sum(sum(squaredErrorImage1)) / (R * C);
PSNR1 = 10 * log10( 255^2 / mse1);
r2=rand_index(f_m);
gce2 = global_consistancy_error(f_m);
jaccardIndex_ac2 = sum(x(:) & u(:)) / sum(x(:) | u(:));
message = sprintf('The mean square error for noisy image is %.2f.\nThe PSNR = %.2f.\nThe Rand_index = %.2f.\nThe global_consistancy_error = %.2f.\nThe jaccard coefficient = %.2f.\nThe jaccard distance = %.2f.', mse1, PSNR1, r2, gce2,jaccardIndex_ac2,1-jaccardIndex_ac2);
msgbox(message);
x1=logical(x);
y1=logical(y);
n=jaccard_coefficient(x1,y1);