Define Image enhancement.

Image enhancement is a process in digital image processing that aims to improve the visual quality or interpretability of an image for human perception or for facilitating computer-based analysis. The goal is to highlight specific features, improve contrast, reduce noise, and enhance overall visibility of important information in the image. Image enhancement techniques are applied to a wide range of fields, including medical imaging, satellite imagery, surveillance, and forensic analysis.

Key Aspects of Image Enhancement:

1. Contrast Enhancement:
   Contrast enhancement involves adjusting the distribution of pixel intensity values in an image to increase the visual distinction between different features. This helps bring out details that might be obscured in the original image.

2. Brightness Adjustment:
   Modifying the overall brightness of an image is a fundamental aspect of enhancement. It involves scaling the pixel values to make the image visually more appealing or to improve visibility in specific regions.

3. Histogram Equalization:
   Histogram equalization redistributes pixel intensity values across a broader range to enhance the overall contrast. This technique is particularly effective for images with limited contrast or uneven intensity distributions.

4. Spatial Filtering:
   Spatial filtering involves applying convolution operations using masks or kernels to accentuate or suppress specific spatial features in an image. Techniques like edge enhancement and smoothing fall under spatial filtering.

5. Frequency Domain Techniques:
   Transformations in the frequency domain, such as Fourier transforms, can be used for image enhancement. Filtering operations in the frequency domain can help emphasize or suppress certain frequency components, contributing to sharpening or blurring effects.

6. Color Enhancement:
   In color images, enhancement can be applied to individual color channels or to the image as a whole. This helps in emphasizing certain colors or improving the overall vibrancy of the image.

7. Dynamic Range Adjustment:
   Adjusting the dynamic range involves mapping the original intensity values to a new range to ensure that important details are not lost in areas with extreme brightness or darkness.

8. Adaptive Enhancement:
   Adaptive enhancement methods dynamically adjust enhancement parameters based on the local characteristics of the image. This allows for a more tailored approach to different regions within the image.

9. Image Fusion:
   Image fusion combines information from multiple images or sensors to create a composite image that incorporates the strengths of each source. Fusion enhances overall information content and facilitates more comprehensive analysis.

Image enhancement is a crucial preprocessing step in various applications, including medical diagnostics, satellite image interpretation, surveillance, and computer vision tasks. It aims to improve the quality of visual information, aiding both human interpretation and the effectiveness of subsequent computer-based algorithms and analyses.