![Spatial domain methods:
• The term spatial domain refers to the aggregate of pixels composing
an image.
• Spatial domain methods are procedures that operate directly on these
pixels.
• Spatial Domain processes will be denoted by the expression ,
g(x,y)= T[f(x,y)]
Where, g is the output, f is the input image and T is an operation on f
defined over some neighborhood of (x,y)](https://image.slidesharecdn.com/aip-230717170723-be31cb09/85/aip-pptx-5-320.jpg)
![Image Negtive
Negative images are useful for enhancing white or grey
detail embedded in dark regions of an image.
The negative of an image with gray levels in the range
[0,L-1] is obtained by using the expression
s = L -1 - r
L-1 = Maximum pixel value .
r = Pixel value of an image.](https://image.slidesharecdn.com/aip-230717170723-be31cb09/85/aip-pptx-8-320.jpg)
aip.pptx
- 1. Image enhancement Group member Hina, Samreen and Shahida
- 2. Image Enhancement • Image enhancement refers to a set of techniques and processes aimed at improving the visual quality and overall appearance of an image. The goal is to enhance specific aspects of the image, such as brightness, contrast, sharpness, color accuracy, and noise reduction, to make it more visually appealing or suitable for specific applications.
- 3. Introduction: • Image Enhancement is the process of manipulating an image so that the result is more suitable than the original for a specific application. • The goal is to enhance specific aspects of the image, such as brightness, contrast, sharpness, color accuracy, and noise reduction, to make it more visually appealing or suitable for specific applications.
- 5. Spatial domain methods: • The term spatial domain refers to the aggregate of pixels composing an image. • Spatial domain methods are procedures that operate directly on these pixels. • Spatial Domain processes will be denoted by the expression , g(x,y)= T[f(x,y)] Where, g is the output, f is the input image and T is an operation on f defined over some neighborhood of (x,y)
- 6. Cont… According to the operations on the image pixels, it can be further divided into 2 categories: 1. Point operations 2. Spatial operations
- 7. Point Opeation Operation deals with pixel intensity values individually. The intensity values are altered using particular transformation techniques as per the requirement. The transformed output pixel value does not depend on any of the neighbouring pixel value of the input image. Examples: Image Negative. Log Transformation Thresholding. Brightness Enhancement. Log Transformation. Power Law Transformation 3
- 8. Image Negtive Negative images are useful for enhancing white or grey detail embedded in dark regions of an image. The negative of an image with gray levels in the range [0,L-1] is obtained by using the expression s = L -1 - r L-1 = Maximum pixel value . r = Pixel value of an image.
- 9. Cont… Fig: Example of image inversion
- 10. Log Transformation The log transformation is given by the expression • s = c log(1 + r) • where c is a constant and it is assumed that r≥0. This transformation maps a narrow range of low- level grey scale intensities into a wider range of output values. Similarly maps the wide range of high-level grey scale intensities into a narrow range of high level output values. This transform is used to expand values of dark pixels and compress values of bright pixels.
- 11. Logarithmic Transformaion Contd… Original Image Transformed Image
- 12. Techniques • Image Deblurring • Image filtering • Image sharping • Noise Reduction • Contrast enhancement
- 13. Image Debluring • Image deblurring refers to the process of reducing or removing blur from an image to restore its sharpness and clarity. Blurring in images can occur due to various factors such as motion blur, camera shake, defocus blur, or lens aberrations.
- 14. Types of image blurring • Motion Blur • Defocus Blur
- 15. Motion blur • Motion blur occurs due to the relative movement between the camera and the subject during exposure.
- 16. Defocus blurring Defocus blur is caused by incorrect focus settings limited depth of field.
- 17. Image Filtering • Image filtering is a fundamental technique used in image processing to modify or enhance images by applying certain operations to each pixel or a group of pixels. – Convolution – Correlation
- 18. Noise Reduction • Noise reduction, on the other hand, is the process of minimizing or eliminating unwanted random variations or distortions that degrade the quality of an image
- 19. Image sharping • Image Sharpening is a technique for increasing the apparent sharpness of an image. • It enhance edges and fine details.
- 20. Contrast stretching • Contrast stretching is a simple technique to enhance the contrast of a digital image by mapping the pixel values to a wider range.
Editor's Notes
- #18: convolution is defined as it is defined as the integral of the product of the two functions after one is reversed and shifted. On the other hand, cross-correlation is known as sliding dot product or sliding inner-product of two functions. The filter in cross-correlation is not reversed.