{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Image Enhancement -Part 1

Image Enhancement -Part 1 - Image Enhancement Part 1 1...

This preview shows pages 1–20. Sign up to view the full content.

1 Image Enhancement Part 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 Image Enhancement Used to emphasize and sharpen image  features for display and analysis. enhance otherwise hidden information Filter important image features Discard unimportant image features Enhancement method are application  specific.
3

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
4 Imput Image I(r,c) Frequency Domain Output Image E(r,c) Spatial Domain Application-Specific Feedback
5 Image Enhancement In Spatial Domain Refers to the image plane  (the ‘natural’ image) Direct image manipulation

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Remember ? A 2D grayvalue - image is a 2D -> 1D function, v = f(x,y)
Remember ? As we have a function, we can apply operators to this function, e.g. T(f(x,y)) = f(x,y) / 2 Operator Image (= function !)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Remember ? As we have a function, we can apply operators to this function, e.g. T(f(x,y)) = f(x,y) / 2 Operator Image (= function !)
Remember ? T transforms the given image f(x,y) into another image g(x,y) f(x,y) g(x,y)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Spatial Domain The operator T can be defined over The set of pixels (x,y) of the image The set of ‘neighborhoods’ N(x,y) of each pixel A set of images f1,f2,f3,…
Operation on the set of image-pixels 6 8 2 0 12 200 20 10 3 4 1 0 6 100 10 5 Spatial Domain (Operator: Div. by 2)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Operation on the set of ‘neighborhoods’ N(x,y) of each pixel 6 8 2 0 12 200 20 10 226 Spatial Domain 6 8 12 200 (Operator: sum)
Operation on a set of images f1,f2,… 6 8 2 0 12 200 20 10 Spatial Domain 5 5 1 0 2 20 3 4 11 13 3 0 14 220 23 14 (Operator: sum)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Operation on the set of image-pixels Remark: these operations can also be seen as operations on the neighborhood of a pixel (x,y), by defining the neighborhood as the pixel itself. The easiest case of operators g(x,y) = T(f(x,y)) depends only on the value of f at (x,y) T is called a gray-level or intensity transformation function Spatial Domain
Basic Gray Level Transformations Image Negatives Log Transformations Power Law Transformations Piecewise-Linear Transformation Functions For the following slides L denotes the max. possible gray value of the image, i.e. f(x,y) [0,L] Transformations

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Image Negatives: T(f)= L-f Transformations Input gray level Output gray level T(f)=L-f
Log Transformations: T(f) = c * log (1+ f) Transformations Log/ Expand gray levels InvLog/ Compress gray levels

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Log Transformations Transformations
Power Law Transformations T(f) = c*f γ Transformations

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern