Image Processing

Image processing involves the manipulation and analysis of visual information in digital images. It encompasses a wide range of techniques aimed at enhancing, interpreting, and extracting useful information from images for various applications, including computer vision, medical imaging, and multimedia systems.

Methods and Techniques

• Histogram Equalization - Adjusts the distribution of pixel intensities in an image to enhance contrast and improve visibility of details.

• Image Filtering (e.g., Gaussian, Sobel) - Applies convolution operations to smooth or enhance images, commonly used for noise reduction, edge detection, and feature extraction.

• Morphological Operations - Utilizes operations like dilation and erosion to analyze and manipulate the structure of shapes in an image.

• Edge Detection (e.g., Canny, Laplacian) - Identifies boundaries and edges in an image, useful for feature extraction and object recognition.

• Image Compression (e.g., JPEG, PNG) - Reduces the storage space and transmission bandwidth required for images while preserving essential visual information.

• Color Space Conversion (e.g., RGB to HSV) - Converts the representation of colors in an image to facilitate specific analysis or enhance visual perception.

• Image Registration - Aligns and overlays multiple images to enable comparison or combination of information from different sources.

• Image Restoration - Aims to recover the original image from degraded versions, addressing issues like blurring or noise.

• Super-Resolution Techniques - Enhances the resolution of an image, often using deep learning models to generate high-quality details.