Image processing - Histogram Equalization
- 1. Image Processing Report Histogram Equalization
- 2. Image Processing - Report #1 Due date: April. 2, 2018 In our class, we have learned about a histogram equalization technique to enhance the images's quality. Histograms are the basics for numerous spatial domain techniques. Simple examples can help you to understand how to equalize the histogram in an image. Suppose that Fig. 1 is a 4-bit input image(L=16) of size 66 pixels. And also we assume that 0 is black and 15 is white. Now calculate the histogram equalized image. You need to calculate histogram, PDF(probability density function), CDF(cumulative density function), and the table for converting 駆 to 月. When you calculate s values, 月, you round them to the nearest integer. Finally, explain the result comparing to the input image in point of their contrasts. You have to submit the report in class. 1. The Histogram of the input image. 駆 0 1 2 3 4 5 6 7 frequency 13 12 6 3 2 0 0 0 駆 8 9 10 11 12 13 14 15 frequency 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 3 3 3 4 4 0 Fig.1 Input image
- 3. 2. PDF(probability density function) 3. CDF(cumulative density function) 4. the table for converting 駆 to 月 ( (prev Expression) * (L-1) ) 5. The histogram equalized image 駆 0 1 2 3 4 5 6 7 喜 駆 13/36 12/36 6/36 3/36 2/36 0 0 0 駆 8 9 10 11 12 13 14 15 喜 駆 0 0 0 0 0 0 0 0 駆 0 1 2 3 4 5 6 7 ь喜 駆 13/36 25/36 31/36 34/36 1 1 1 1 駆 8 9 10 11 12 13 14 15 ь喜 駆 1 1 1 1 1 1 1 1 駆 0 1 2 3 4 5 6 7 月 13/36 *15 25/36 *15 31/36 *15 34/36 *15 15 15 15 15 駆 8 9 10 11 12 13 14 15 月 15 15 15 15 15 15 15 15 5.4 5.4 5.4 5.4 5.4 5.4 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 3 3 3 4 4 0 Fig.1 Input image
- 4. 螳 蠏殊 襦 覦襴 6. Explain the result comparing to the input image in point of their contrasts. 牛 觜 覦 蠏Histogram Equalization Matrix Gray 螳 觜蟲 螻襯 覿 螳讌 襦 覦蠖Level Histogram Matrix . 讌襷 覲 襷 螳讌螻 觜蟲 螳煙 伎蠍 覓語 , 願骸 螻襴讀 貊襦 覲 譯殊伎 襯 螳讌螻, Matrix 5.4 5.4 5.4 5.4 5.4 5.4 10.4 10.4 10.4 10.4 10.4 10.4 12.9 12.9 12.9 12.9 12.9 12.9 10.4 10.4 10.4 10.4 10.4 10.4 14.1 14.1 14.1 15 15 5.4 Fig.2 Output image 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 13 13 13 13 13 13 10 10 10 10 10 10 14 14 14 15 15 5 Fig.3 Output image with rounded ヮ護月 0 1 2 3 4 5 6 7 frequency 0 0 0 0 0 13 0 0 ヮ護月 8 9 10 11 12 13 14 15 frequency 0 0 12 0 0 6 3 2 Fig.4 Histogram of the Output image
- 5. 一一 讌Histogram Equalization . 觜襯 觜蟲 蠍一ヾ 觜, Matrix Gray Level Matrix 蠏狩伎り 覲 . 讌襷 譯殊伎 朱 蠍 覓語Matrix shape (6, 6) Histogram 牛 螳 覿襯 所 蠍郁 曙 Equalization (Enhanced) . 磯殊 譟郁 襦 殊 螳 螻襴讀, lena.png 伎 Histogram Equalization . 蟆郁骸 れ螻 螳.
- 6. 蠍一ヾ 蠏碁Μ螻 觜伎 觜襯0~50, 225~250 Gray Level 牛 豌 觜螳 螻襯願 覿Equalization 0~255 Gray Level 蟆 螻觜 朱 覲 蟆 誤 ( ) . 牛 豢 螳れ , Histogram Equalization Matrix Size 襦 螳 覦螳螻 觜訣 讌る 蟆 Max Gray Level(2^Bit 1) . 1) 6*6 , (5*13 + 10*12 + 13*6 + 14*3 +15*2) / (6*6) = 9.3 16/2 = 8 2) lena.png , 33743418/ (512*512) = 128.7209 256 / 2 = 128 譬 覦一伎 レ朱 譯殊 蠏 蠏殊螳 貉れり 覲 .
- 7. 磯殊 れ螻 螳 豢 朱 蠏殊螳 貉れ, , L/2 襦 觜螳 螻襯願 覿覦磯 る 蟆 Gray Level . ь ヮь 駆ヮ 作溢月餓駆去克餓ь月餓__