Digital Image Processing Ch3 Intensity transform and spatial filtering [1]

Spatial domain vs. Frequency domain

Change the intensity of each pixel in order to enhance the image

 

Simplest form : Intensity transform with smallest box 1x1

 

Some basic gray-level transformation functions used for image enhancement.

 

Gamma Transformation : gamma > 1 : image getting darker

 

• Piecewise-Linear Transformation Function

S=T(r)

장점 : the form of function 임의적으로 complex할 수 있다.

Contrast stretching -> slope < 1, contrast down, slope > 1, contrast enhance

단점 : trade off 발생 - 가운데에만 contrast를 높일 수 있고 그것보다 작거나 크면 오히려 contrast가 줄어든다

       : 즉, 모든 부분에서 contrast를 증가시킬 수 없다(not uniformly)

Full range linear stretching < - y=x in r1 to r2

Threshold (intensity transformation) function <- y=step(x), counting cells

Gray-level slicing : highlighting a specific range of gray levels, 

 

• How can we preserve kidney and blood vessel?

input S=T(r) Angiogram 

Slicing left intact : shape of the flow of the contrast medium to detect blockages

Gray leftA~B darker, other intact : actual flow of the contast mediumas a function of time

Bit -Plane slicing

mainly used for data compression and progressive transmission

MSB : b7 * 2^7 

LSB : b0 * 2 ^0

b7, b6, b5 forms general shape(big storage information) and under that area gives details(less important)

plane 8~5만 sum해도 input이랑 굉장히 비슷해진다. 

• 시험에 나오는 Histogram Processing

히스토그램은 무엇인가? : intensity는 각각 occurance를 몇개를 갖고 있냐에 대한 식

nomalized histogram : probability of occurences

히스토그램을 통해서 image가 밝은지 어두운지 contrast가 높은지 낮은지를 알 수 있다. 

Desired image quality : broad and nearly uniformly distributed