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Writer's pictureAritzon Global

Digital Image Processing: Overview



Image Enhancement:
  • Adjusts the contrast of an image by redistributing pixel intensities.

  • Applies convolution masks (e.g., mean filter, Gaussian filter) to perform smoothing or sharpening operations.

  • Utilizes techniques such as Fourier transform to enhance images by manipulating their frequency content.


Image Restoration:

  • Techniques like median filtering, Wiener filtering, or adaptive filtering are employed to reduce noise in images.

  • Techniques to remove blur caused by motion, defocus, or other factors.


Image Segmentation:

  • Divides an image into foreground and background based on a specified threshold.

  • Groups pixels into regions based on similarity criteria.

  • Uses algorithms like k-means clustering to partition an image into distinct clusters.


Feature Extraction:

  • Detects sharp changes in intensity to identify object boundaries (e.g., Sobel, Canny edge detectors).

  • Identifies key interest points in an image (e.g., Harris corner detector).

  • Identifies regions of interest with similar properties (e.g., scale-invariant feature transform - SIFT).


Image Classification and Recognition:

  • Uses labelled training data to classify images using techniques like support vector machines (SVM), neural networks, or decision trees.

  • Clusters images into groups based on similarities without labelled data (e.g., k-means clustering).

  • Utilizes deep neural networks for tasks such as image classification, object detection, and segmentation.


Morphological Processing:

  • Basic operations to remove or add pixels based on the shape of a structuring element.

  • Combines erosion and dilation operations for noise reduction and boundary smoothing.


Image Registration:

  • Aligns images from different sources or at different times through translation, rotation, scaling, or affine transformations.

  • Matches images based on their intensity patterns to correct for geometric or intensity distortions.


Image Compression:

  • Reduces the size of an image file without losing any information (e.g., run-length encoding, Huffman coding).

  • Reduces file size by removing redundant information, resulting in some loss of image quality (e.g., JPEG compression).

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