IMAGE ENHANCEMENT
Image enhancement:
Image enhancement is the process of improving the quality of an image to make it more visually appealing or to extract important information. Image enhancement can be done in two domains: spatial domain and frequency domain.
Spatial Domain:
Spatial domain enhancement techniques operate directly on the pixels of an image. The following are some of the commonly used techniques in spatial domain:
- Gray level transformations: These are simple mathematical functions that map the input gray levels to output gray levels to enhance the contrast and brightness of an image.
- Histogram processing: Histogram is a graph that represents the frequency distribution of pixel intensities in an image. Histogram processing techniques include histogram equalization, contrast stretching, and adaptive histogram equalization.
- Smoothing and Sharpening Spatial Filtering: Smoothing techniques are used to remove noise from the image while sharpening techniques are used to enhance the edges in the image. Commonly used filters include mean, median, and Gaussian filters.
Frequency Domain:
Frequency domain enhancement techniques involve transforming the image from spatial domain to frequency domain using Fourier Transform. The following are some commonly used frequency domain filters:
- Ideal, Butterworth and Gaussian filters: These filters are used to remove or attenuate certain frequency components in an image. The selection of filter type and its parameters depends on the desired effect and the characteristics of the input image.
- Homomorphic filtering: This is a technique used to enhance the illumination of an image while preserving the contrast. This is particularly useful in images with non-uniform illumination, such as medical images.
Color image enhancement:
Color image enhancement refers to improving the quality of color images by adjusting their color distribution, brightness, and contrast. Color images are captured using devices such as cameras and scanners, and may contain color distortions, noise, and other artifacts. Color image enhancement techniques aim to improve the visual appearance of these images and make them more suitable for a wide range of applications such as image processing, computer vision, and digital multimedia.
Color image enhancement techniques can be broadly classified into two categories: spatial domain and frequency domain. Spatial domain techniques operate directly on the pixel values of the image, while frequency domain techniques operate on the image's Fourier transform. Some commonly used color image enhancement techniques are:
1. Histogram equalization: This technique adjusts the distribution of pixel values in an image to enhance its contrast and improve its visual appearance.
2. Color balancing: This technique adjusts the color balance of an image by manipulating the red, green, and blue (RGB) channels to correct color casts and improve color accuracy.
3. Color correction: This technique adjusts the color temperature and tint of an image to make it look more natural and pleasing to the eye.
4. Color filtering: This technique selectively enhances or suppresses certain colors in an image to highlight specific features or improve its overall appearance.
Spatial Domain:
Spatial domain refers to the spatial arrangement of pixels in an image. Spatial domain image processing techniques operate directly on the pixel values of an image to enhance its visual appearance. Some commonly used spatial domain techniques are:
- Gray level transformations: This technique adjusts the brightness and contrast of an image by mapping its pixel values to a new range.
- Histogram processing: This technique adjusts the distribution of pixel values in an image to enhance its contrast and improve its visual appearance.
- Smoothing and sharpening filters: These filters are used to remove noise and blur from an image, and enhance its edges and details.
Frequency Domain:
Frequency domain refers to the representation of an image as a combination of sine and cosine waves of different frequencies. Frequency domain image processing techniques operate on the Fourier transform of an image to enhance its visual appearance. Some commonly used frequency domain techniques are:
- Fourier transform: This technique decomposes an image into its frequency components and represents it as a sum of sine and cosine waves of different frequencies.
- Frequency domain filters: These filters are used to remove noise and blur from an image, and enhance its edges and details.
- Homomorphic filtering: This technique is used to correct for uneven illumination and color casts in an image by applying a high-pass filter in the frequency domain.
Overall, image enhancement techniques play a crucial role in improving the visual quality and usefulness of digital images, and are widely used in various applications such as medical imaging, remote sensing, and multimedia processing.
Section A: Answer any 5 questions out of 8 (each carrying 5 marks)
1. Explain the steps involved in image enhancement.
2. What is spatial domain filtering? Give an example.
3. Explain the concept of homomorphic filtering.
4. What is color image enhancement? How is it different from grayscale image enhancement?
5. Define Fourier Transform. What is its significance in image processing?
6. Explain the concept of frequency domain filtering.
7. What are the different types of frequency domain filters? Explain their respective characteristics.
8. What is image smoothing? How is it different from image sharpening?
Section B: Answer any 5 questions out of 8 (each carrying 10 marks)
1. Explain the different gray level transformations used in image enhancement.
2. What is histogram processing? Discuss its advantages and limitations.
3. Explain the concept of spatial filtering. What are the different types of spatial filters?
4. What is frequency domain filtering? Explain the ideal, Butterworth, and Gaussian filters.
5. Discuss the concept of homomorphic filtering. What are its advantages and disadvantages?
6. Explain the different techniques used for color image enhancement.
7. What is image restoration? Explain the process of image restoration using inverse filtering.
8. Discuss the different techniques used for image sharpening.
Section C: Answer any 5 questions out of 8 (each carrying 15 marks)
1. Explain the different techniques used for spatial filtering. What are their advantages and limitations?
2. Discuss the different types of frequency domain filters. How do they differ from each other?
3. What is image segmentation? Explain the different techniques used for image segmentation.
4. Discuss the different methods used for image deblurring.
5. Explain the different techniques used for image denoising.
6. What is edge detection? Discuss the different techniques used for edge detection.
7. Discuss the different types of morphological operations used in image processing.
8. Explain the concept of wavelet transform. How is it used in image processing?
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