Computer Graphics & Image Processing Chapter # 9 Morphological Image Processing
Image Processing and Reconstruction · 2019. 9. 13. · Basic Image Processing • This will be a...
Transcript of Image Processing and Reconstruction · 2019. 9. 13. · Basic Image Processing • This will be a...
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EE 264 Spring 2018 Instructor: Sara Abrahamsson ([email protected])
Image Processing and Reconstruction
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Basic Image Processing• This will be a beginners course, and everyone is
welcome, but…
• If you already have a lot of experience, this course may not be useful to you
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Course Layout• Homework vs. Projects vs. Exams?
• What do you want to learn and how?
• How do we set your grades?
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Dates for stuff• Midterm
• Oral presentations
• Home work
• Final — or final project???
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Text Books
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Text Book
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Today
• Introduction to digital images
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Digital Image
A digital 2D image can be described as a:
• …function of two spatial variables f(x,y) where (x,y) denote the planar image coordinates and f() is the amplitude of the image at the given location
• …2D matrix of numerical values that correspond to grayscale levels
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Recording images
(a) Single sensing element. (b) Line sensor. (c) Array sensor.
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Digital Image Processing
• Output can be:
• Another image: g(x,y) = P ( f(x,y) )
• A set of attributes (edges, segments, objects)
• An algorithmic “understanding” of the image content
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Why process images?• Storage and sharing
• (Compression, Transmission)
• Display
• (Resize, Optimize contrast and color)
• Enhance and restore
• Extract information
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Software
• MATLAB
• Expensive
• Toolboxes are extra, and there are *many*
• ImageJ / FIJI (Fiji is just ImageJ)
• Free, open source
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Bioimaging applications
• Optical microscopy
• MRI
• Photography
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Microscopy
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MRI
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Photography
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Display (“pretty picture”)
• Contrast adjustment
• Color adjustment
• Deblur
• Denoise
• 3D visualize
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Gray levels
• Binary image: Black and White
• Grayscale image
• 8 bit
• 16 bit
• 32 bit
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Bit depth
• 8 bit image: 2^8 = 256 values
• The largest number you can represent with 8 bits is 11111111 (255 in decimal notation)
• Since 00000000 is the smallest, you can represent 256 things with a byte.
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Image displayed in 32, 16, 8, 4, and 2 intensity levels.
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Number of pixels or in printing: dots per inch (dpi)
(a) Image reduced to 72 dpi and zoomed back to its original 930 dpi using nearest neighbor interpolation. This figure is the same as Fig. 2.23(d). (b) Image reduced to 72 dpi and zoomed using bilinear interpolation. (c) Same as (b) but using bicubic interpolation.
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Sampling
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Noise
(a) Sample noisy image of the Sombrero Galaxy. (b)-(f) Result of averaging 10, 50, 100, 500, and 1,000 noisy images, respectively. All
(a) Noisy image of the Sombrero Galaxy. (b)-(f) Result of averaging 10, 50, 100, 500, and 1,000 noisy images, respectively. All images are size 1548x2238 pixels and all scaled so intensities span the full [0, 255] intensity scale.
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Computational Denoising
Denoised using ROF denoise in FIJI
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Tricks with multiple images
(a) Difference between the 930 dpi and 72 dpi images in Fig. 2.23. (b) Difference between the 930 dpi and 150 dpi images. (c) Difference between the 930 dpi and 300 dpi images.
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Digital Subtraction Angiography
Digital subtraction angiography. (a) Mask image. (b) A live image. (c) Difference between (a) and (b). (d) Enhanced difference image.
Image courtesy of the Image Sciences Institute, University Medical Center, Netherlands(from our textbook: Digital Image Processing in Matlab)
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Shading correction
Shading correction. (a) Shaded test pattern. (b) Estimated shading pattern. (c) Product of (a) by the reciprocal of (b). (See Section 3.5 for a discussion of how (b) was estimated.)
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Inverse lookup table
(a) 8-bit image. (b) Intensity transformation function used to obtain the digital equivalent of a “photographic” negative of an 8-bit image. The arrows show transformation of an arbitrary inputintensity value z into its corresponding output value s0. (c) Negative of (a) obtained using (b)
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Color Images
blue
green red
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Forming a vector
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Synthetic lookup tables
“Red Hot” in FIJI
Chasing the right one can make it easier to see stuff — and to get published…
Inverse
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Logical Operators
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Affine transformation
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Interpolation
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Enlarge
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Fourier Transform
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Filtering
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Thursday is Lab
• We will try out our computer classroom
• We will use MATLAB and ImageJ / FIJI