Homework Chuyende2014

8/10/2019 Homework Chuyende2014

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HOMEWORK

HW1.I/O image

a) Load the image cameraman.tif, using imread(), and show it using imshow().

b)Get the type of the loaded image data using MATLAB function class(), and get the

maximum and minimum data value for this image using MATLAB function max() and

min().

c) Get the breadth and height of the image, using MATLAB functionsize().

d) Get the values of light intensities of pixels [50:100, 50:100] and then show its

corresponding image patch.

e) Produce the following image.

chon loai tai anh cua

chieu rong

chieu cao

cuong do sng

anh dong vi rap noi


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HW2.Color image

a) Load the image peppers.png and show it.

b) To understand how three colors RGB form the image, display each color component

image separately.

xai imread() , imshow()

rieng


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c) Access R matrix and show the R values of pixels [50:100,50:100].

HW3. RGB, grayscale and binary images

a) Create and show the binary image [0 1; 1 0] with the proper magnification.

chia anh ra 4 mau

truy cap ma tran R va bieu dien gia tri R cua pixels

tao va bdien anh nhi phan

do phong dai rieng


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b) Create and show the grayscale image [0 16 32 64 128 255; 255 128 64 32 0] with the

proper magnification. ote:by default, the range is 0 to 1 (type: double); however, you

can convert the image values from double type to unsigned integer 8 bitstype by using

unit8().

c) Convert peppers.pngimage to grayscale image using MATLAB function rgb2gray().

d) Convert the above gray image to binary image by thresholding with the threshold

values 50, 100, 150, 200, 250. MATLAB function to convert a gray image to a binary

image is im2bw().

HW4. Quantization

A typical digital image has pixel values between 0 and 255 represented in 8-bit numbers.

Now, suppose you are allowed to have only 5 bits to represent each pixel value, which

can only represent 32 different codes, but the pixel value should still be between 0 and

255 for display. The codes should be assigned to represent values in order to minimize

the mean square error when the original pixel values are uniformly distributed between 0

and 255.

The following is an example original 2x2 image represented in 8-bit pixels.

255 87

150 30

vung

chuyen gia tri image tu

sang cai grayscale

gia tri nguonganh den trang anh nhi phan

luong tu hoa

duoc bieu dien

chi cho 5 bit de bieu dien 1 diem anh

mu 5 =32 codec nhau

gan

sai so binh phuong trung binh

anh goc 2x2 duoc bieu dien bang 8 bit pixels


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With the new 5-bit codes, what are the values of the resulting image? What are the pixel

values of the resulting images if you only have 3-bit codes?

HW5. Flipped and negative images

a) Guess how the following images look like when compared to the original image

x[n,m]

(i) x[-n+1, m]

(ii) x[n, M-m+1]

(iii) x[-n+1, M-m+1]

where n = 1N, m = 1M, (N,M): size of original image.

Use lena.bmp as the original image (i.e. x[n,m]). Verify your guesses by displaying

resulting images of (i)(ii)(iii) in your report.

Use helpfliplr() andflipud() to see more information.

b) Produce the negative of the lena.bmp image. Describe the visual appearance of the

final result and speculate how this might be used in some practical applications (science,

movies, stc.)

HW6. Salt-pepper noise

Read in HW6.gif image, which contains salt-pepper (shot) noise.

dich anh ?

bieu thuc nao vs 5 bit code

du doan khi so sanh voi anh goc

bieu dienhien thi

ket qua tren bao cao

san xuat dang ve

dau co

nhieu muoi tieu

tac dung cua am ban trong y hoc, khoa hoc,phim anh


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a)

Convolve with a box filter (all ones) to reduce the noise. Select a reasonable size totrade off noise and blurring. Display and submit.

b)Now apply a 3 x 3 and 5x5 median filter to the image and compare the result with the

convolutional filter. Display and submit. Which approach is more satisfying in terms of

image quality ?

HW7. Sharpening a blurred image

Read in a 512x512 blurred image HW7.jpg, which should look like it was taken from a

defocused camera. Try the spatial domain high-boost filter on this image and report your

result.

chap voi bo loc de giam nhieu muoi tieu

giam nhoebo loc trung vi

bo chap tiep can thoa man

lam ro anh bi nhoe

thay A cho toi khi anh nohet nhoachap voi anh goc


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HW8. Histogram equalization

Read in the low contrast image HW8.jpg. Perform the histogram equalization for the

image. Note: due to the quantization of the image levels, you will not be able to achieve a

perfectly flat histogram. In fact, it will have large 'holes' and 'spikes' in it. It is OK to useMatlab histogram equalization command.

HW9.Power-law transformation

Based on the low contrast image HW8.jpg again, please use the power-law transform,

i.e.,s= c.r, to enhance the image. First, you have to decide the value of power (should

be > 1 or < 1?), then appropriately select the scaling constant c to ensure the resulting

gray level values fall within [0, 255]. Please choose three different values and report

which of the three images is most pleasing to your eye, and why? Please also show the

resulting images and the corresponding histograms.

HW10. Morphological operations (done)

anh co do tuong phan thap dung phep bien doi power- law

hinh thai hoat dong


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Documents

Homework Chuyende2014