Image Manipulation Flashcards
Types of image enhancement?
Spatial Domain methods –> based on direct manipulation of pixels in image
Point, local and global image operations are all spatial domain methods
Frequency Domain methods –> Based on modifying the Fourier transform of the image
Filtering is implemented in frequency domain by multiplication
Combination Methods –> combo of spatial domain methods and frequency domain methods
How to calculate the brightness of a gray scale image?
Average intensity of all pixels in the image
Exposure
Amount of light that enters the lens of the camera.
Types of exposure:
1. Overexposure
2. Underexposure
3. Long Exposure (captures a subject over an extended period of time)
What is the difference between brightness and exposure?
Exposure is the amount of light that enters the lens of the camera, while brightness is how bright an object appears in an image
Brightness is a product of exposure
What is good contrast?
Widely spread intensity values and a large difference between the max and min intensity values
What is HDR (High Dynamic Range)
technique that produces images with a larger dynamic range of luminosity than SDR (standard dynamic range)
What is dynamic range
Range of lightest and darkest tones in an image
What is wide dynamic range?
When you are able to see details in both light and dark areas
How do you obtain a HDR image?
Use photographs of a scene taken with different exposure values and combine them.
After the HDR image has been merged, it has to be converted back to 8-b to view on usual displays
What are the characteristics of image operations?
Point –> Output value at specific coordinate is dependent only on the input value at the same coordinate.
Local –> Output value at a given coordinate is dependent on the input values of the neighborhood of that same coordinate
Global –> Output value at given coordinate is dependent on all the values of the input image
Point operations
Type of image operation:
Changes a pixel’s intensity value based on some function f.
New pixels intensity depends on:
Pixel’s previous intensity
Mapping Function.
Examples of point operations:
Histogram Equalization
Gamma correction
What is an image negative?
Produced by subtracting each pixel from the maximum intensity value.
e.g. for an 8-bit image, max intensity is 2^8 - 1 = 255.
So, subtract each pixel’s intensity value from 255
What is the Power Law Transformation / Gamma correction?
Point operation
It adjusts the brighnress of an image using gamma correction:
O = 255 × (I/255)G
O = output image [0, 255]
I = Input Image
G = Gamma (controls brightness of image)
If Gamma < 1, Darker input values are mapped to brighter output values)
If Gamme > 1, Brighter input values mapped to darker output values
Histogram Equalization
Technique for adjusting image intensities to enhance contrast. Transforms an image so that its histogram is more evenly distributed across the entire range of values
Contrast Stretching
image normalization with a piece wise linear transformation function
Piece wise Transformation
Spatial Domain method used for enhancing a group of pixels in a defined range.
Local Operations
Types of local operations:
Linear Filter –> Outputted pixel value is determined as a weighted sum of input pixel values.
The entries in the weight kernal are called filter coefficients.
Non-linear Filters –> Use the kernal to obtain the neighboring pixel values, and then uses ordering mechanisms to produce the output pixel
Kernal / Filter / Spatial Mask
Rectangle / matrix of values used in the convolution process to modify an image
Convolution
Involves multiplying the Kernal values with the corresponding pixel values in the image. The resulting values are added together and the sum is used to replace the value of the central pixel in the output image.
Process is repeated for every pixel in the image
Why is padding necessary after applying a filter
Filtered images often suffer from boundary effects. Padding is used to deal with this
Padding
Types of Padding:
Zero –> Set all pixels outside source image to 0.
Constant –. Set all pixels outside source image to specified border value
clamp –> repeat edge pixels indefinitely
Mirror –> Reflect pixels accross the image edge
Extend
Types of linear filters:
Box Filter –> Averages the pixel values in a KxK window. Each pixel is computed as the average of it’s surrounding pixels (low-pass filter)
Gaussian Filter –> Uses a Gaussian Kernal (square matrix of pixels where the pixel values correspond to the values of a Gaussian Curve) (low-pass filter)
Low pass filters
Used to remove high spatial frequency noise from an image
Non-Linear filters
As the kernal is shifted around the image, the order of the pixel sin the window section of the image is rearranged, and the output image is produced from these rearranged pixels.
Types of Non-Linear Filters:
1. Median Filter
2. Bilateral Filter