12 Questions
The formula for image negative is s = L + 1 - r where s is the output pixel value, L is the maximum possible pixel value, and r is the input pixel value.
False
Histogram equalization is a technique to reduce noise in an image.
False
Power law transformations can be used to brighten an image.
True
Contrast stretching is used to reduce the contrast of an image.
False
Intensity level slicing is a technique to adjust the brightness of an image.
False
Image negative is used for image compression.
False
What is the purpose of applying image negative technique to an image?
To enhance details in bright regions of an image and improve visibility of dark objects on a bright background.
What is the primary goal of histogram equalization technique in image processing?
To improve the contrast of an image, especially in cases where the image is over or under-exposed.
What is the primary application of power law transformations in image processing?
To improve the contrast of an image, especially in cases where the image has a large dynamic range.
What is the main purpose of contrast stretching in image processing?
To improve the visibility of details in images with a limited contrast range or high dynamic range.
What is the primary use of intensity level slicing in image processing?
To segment an image into regions based on pixel intensity, separating objects in an image based on their brightness.
What is the primary advantage of bit plane slicing in image processing?
To compress an image by storing only the significant bit planes, and perform image filtering and feature extraction.
Study Notes
Image Processing Techniques
Image Negative
- Definition: An image negative is a processed image where each pixel value is inverted
- Formula:
s = L - 1 - rwheresis the output pixel value,Lis the maximum possible pixel value, andris the input pixel value - Effect: Reverses the brightness of the image, making dark areas bright and bright areas dark
- Used for: Image enhancement, feature extraction, and noise reduction
Histogram Equalization
- Definition: A technique to adjust the contrast of an image by redistributing the pixel values
- Goal: To produce an image with a uniform histogram, making the image more visually appealing
- Process:
- Calculate the histogram of the input image
- Calculate the cumulative distribution function (CDF) of the histogram
- Apply the CDF to the input image to get the output image
- Effect: Improves the contrast of the image, making it more readable and visually appealing
Power Law Transformations
- Definition: A technique to adjust the brightness and contrast of an image using a power law function
- Formula:
s = c \* r^γwheresis the output pixel value,cis a constant,ris the input pixel value, andγis the power law exponent - Effect: Can be used to brighten or darken the image, and to adjust the contrast
- Used for: Image enhancement, feature extraction, and noise reduction
Contrast Stretching
- Definition: A technique to adjust the contrast of an image by stretching the pixel values
- Formula:
s = (r - min) / (max - min) \* (L - 1)wheresis the output pixel value,ris the input pixel value,minandmaxare the minimum and maximum pixel values, andLis the maximum possible pixel value - Effect: Improves the contrast of the image, making it more readable and visually appealing
- Used for: Image enhancement, feature extraction, and noise reduction
Intensity Level Slicing
- Definition: A technique to segment an image into different regions based on intensity levels
- Process:
- Divide the image into different intensity levels
- Assign a specific color or gray level to each intensity level
- Effect: Highlights specific features or regions of interest in the image
- Used for: Image segmentation, feature extraction, and object recognition
Bit Plane Slicing
- Definition: A technique to decompose an image into its constituent bit planes
- Process:
- Divide the image into its individual bit planes (e.g. 8-bit image into 8 planes)
- Analyze each bit plane separately
- Effect: Allows for the analysis of individual bits of the image, useful for feature extraction and object recognition
- Used for: Image compression, feature extraction, and object recognition
Explore various image processing techniques including image negative, histogram equalization, power law transformations, contrast stretching, intensity level slicing, and bit plane slicing. Learn how these techniques are used for image enhancement, feature extraction, and noise reduction.
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