Image Processing Techniques

Image Processing Techniques

Image Enhancement

• Histogram Equalization: adjusts image contrast by redistributing pixel values
• Contrast Stretching: increases image contrast by stretching pixel values
• Gamma Correction: adjusts image brightness and contrast by applying a power-law transformation

Image Filtering

• Low-Pass Filtering: removes noise and smooths image by averaging neighboring pixels
• High-Pass Filtering: enhances image details by emphasizing high-frequency components
• Median Filtering: reduces noise by replacing pixel values with median of neighboring pixels

Image Restoration

• Deblurring: removes blur caused by motion or lens distortion using inverse filtering
• Noise Reduction: reduces random fluctuations in image intensity using filters or transforms

Image Segmentation

• Thresholding: separates objects from background based on intensity values
• Edge Detection: identifies object boundaries using gradient operators (e.g. Sobel, Canny)
• Clustering: groups pixels into regions based on similarity in intensity or texture

Image Transforms

• Fourier Transform: decomposes image into frequency components for filtering or compression
• Discrete Cosine Transform (DCT): used in JPEG compression to reduce image size
• Wavelet Transform: represents image in multiple scales and resolutions for efficient compression

Other Techniques

• Image Compression: reduces image size using lossy (e.g. JPEG) or lossless (e.g. PNG) algorithms
• Image Denoising: removes noise using filters or machine learning-based methods
• Image Registration: aligns multiple images of the same scene taken at different times or from different viewpoints

Image Processing Techniques

Image Enhancement

• Adjusts image contrast by redistributing pixel values using Histogram Equalization
• Increases image contrast by stretching pixel values using Contrast Stretching
• Adjusts image brightness and contrast by applying a power-law transformation using Gamma Correction

Image Filtering

• Removes noise and smooths image by averaging neighboring pixels using Low-Pass Filtering
• Enhances image details by emphasizing high-frequency components using High-Pass Filtering
• Reduces noise by replacing pixel values with median of neighboring pixels using Median Filtering

Image Restoration

• Removes blur caused by motion or lens distortion using Deblurring and inverse filtering
• Reduces random fluctuations in image intensity using Noise Reduction filters or transforms

Image Segmentation

• Separates objects from background based on intensity values using Thresholding
• Identifies object boundaries using gradient operators (e.g. Sobel, Canny) for Edge Detection
• Groups pixels into regions based on similarity in intensity or texture using Clustering

Image Transforms

• Decomposes image into frequency components for filtering or compression using Fourier Transform
• Reduces image size using Discrete Cosine Transform (DCT) in JPEG compression
• Represents image in multiple scales and resolutions using Wavelet Transform for efficient compression

Other Techniques

• Reduces image size using Image Compression algorithms (lossy: e.g. JPEG, lossless: e.g. PNG)
• Removes noise using filters or machine learning-based methods for Image Denoising
• Aligns multiple images of the same scene taken at different times or from different viewpoints using Image Registration