matlab HSI 格式图像 intensityhuesaturation image归一化有什么作用？怎么实现？

什么叫归一化？怎么联系到HSI格式的？

我理解的归一化是将数据变成某种相对值关系（它是一种无量纲处理手段），例如：将0~255这间的数据double化为0~1（相对值）。

从RGB到HSI只是对同一图像用不同的方式表示，这样有利于使用不同的方法进行处理。

例如：我想将一幅图像的饱和度提高，那么直接用RGB不太容易，于是转化为HSI后，就非常容易了。

下面是转换代码RGB和HSI的互换代码。

--------------------------------------------------------------------------

function

hsi=rgb2hsi(rgb)

%RGB2HSI

Converts

an

RGB

image

to

HSI

%

HSI=RGB2HSI(rgb)

converts

an

RGB

image

to

HSI

The

input

image

is

%

assumed

to

be

of

size

M-by-N-by-3,

where

the

third

dimension

accounts

%

for

three

image

planes:red,

green,

and

blue,

in

that

order

If

all

RGB

%

component

images

are

equal,

the

HSI

conversion

is

undefined

Ths

input

%

image

can

be

of

class

double

(with

values

in

the

rang[0,1]),

uint8,

or

%

uint16

%

The

output

image,

HSI,

is

of

class

double,

where:

%

hsi(:,:,1)=

hue

image

normalized

values

to

the

range

[0,1]

by

%

dividing

all

angle

values

by

2pi

%

hsi(:,:,2)=saturation

image,

in

the

range

[0,1]

%

hsi(:,:,3)=intensity

image,

in

the

range

[0,1]

%Extract

the

individual

component

images

rgb=im2double(rgb);

r=rgb(:,:,1);

g=rgb(:,:,2);

b=rgb(:,:,3);

%Implement

the

conversion

equations

num=05((r-g)+(r-b));

den=sqrt((r-g)^2+(r-b)(g-b));

theta=acos(num/(den+eps));

H=theta;

H(b>g)=2pi-H(b>g);

H=H/(2pi);

num=min(min(r,g),b);

den=r+g+b;

den(den==0)=eps;

S=1-3num/den;

H(S==0)=0;

I=(r+g+b)/3;

%Combine

all

three

results

into

an

hsi

image

hsi=cat(3,H,S,I);

function

rgb=hsi2rgb(hsi)

%HSI2RGB

Converts

an

HSI

image

to

RGB

%

HSI2RGB

Converts

an

HSI

image

to

RGB,

where

HSI

is

assumed

to

be

of

%

class

double

with:

%

hsi(:,:,1)=

hue

image

normalized

values

to

the

range

[0,1]

by

%

dividing

all

angle

values

by

2pi

%

hsi(:,:,2)=saturation

image,

in

the

range

[0,1]

%

hsi(:,:,3)=intensity

image,

in

the

range

[0,1]

%

The

components

of

the

output

image

are:

%

rgb(:,:,1)=red;

%

rgb(:,:,2)=green

%

rgb(:,:,3)=blue

%Extract

the

individaul

HSI

component

images

H=hsi(:,:,1)2pi;

S=hsi(:,:,2);

I=hsi(:,:,3);

%Implement

the

conversion

equations

R=zeros(size(hsi,1),size(hsi,2));

G=zeros(size(hsi,1),size(hsi,2));

B=zeros(size(hsi,1),size(hsi,2));

%

RG

sector

(0<=H<2pi/3)

idx=find((0<=H)&(H<2pi/3));

B(idx)=I(idx)(1-S(idx));

R(idx)=I(idx)(1+S(idx)cos(H(idx))/cos(pi/3-H(idx)));

G(idx)=3I(idx)-(R(idx)+B(idx));

%BG

sector

(2pi/3<=H<4pi/3)

idx=find((2pi/3<=H)&(H<4pi/3));

R(idx)=I(idx)(1-S(idx));

G(idx)=I(idx)(1+S(idx)cos(H(idx)-2pi/3)/cos(pi-H(idx)));

B(idx)=3I(idx)-(R(idx)+G(idx));

%BR

sector

idx=find((4pi/3<=H)&(H<=2pi));

G(idx)=I(idx)(1-S(idx));

B(idx)=I(idx)(1+S(idx)cos(H(idx)-4pi/3)/cos(5pi/3-H(idx)));

R(idx)=3I(idx)-(G(idx)+B(idx));

%Combine

all

three

results

into

an

RGB

image

Clip

to

[0,1]

to

compensate

for

floating-point

arithmetic

rounding

effects

rgb=cat(3,R,G,B);

rgb=max(min(rgb,1),0);

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