Image Processing Algorithms

Till now, we have read about Image processing being a technique to carry out a particular set of actions on an image for obtaining an enhanced image or extracting some valuable information from it. The input is an image, and output may be an improved image or characteristics/features associated with the same.

It is essential to know that computer algorithms have the most significant role in digital image processing. Developers have been using and implementing multiple algorithms to solve various tasks, which include digital image detection, image analysis, image reconstruction, image restoration, image enhancement, image data compression, spectral image estimation, and image estimation. Sometimes, the algorithms can be straight off the book or a more customized amalgamated version of several algorithm functions.

Image processing algorithms commonly used for complete image capture can be categorized into:

Low-level techniques, such as color enhancement and noise removal,

Medium-level techniques, such as compression and binarization,

and higher-level techniques involving segmentation, detection, and recognition algorithms extract semantic information from the captured data.

 

 

Types of Image Processing Algorithms

Some of the conventional image processing algorithms are as follows:

Contrast Enhancement algorithm: Colour enhancement algorithm is further subdivided into -

  • Histogram equalization algorithm: Using the histogram to improve image contrast
  • Adaptive histogram equalization algorithm: It is the histogram equalization which adapts to local changes in contrast
  • Connected-component labeling algorithm: It is about finding and labeling disjoint regions

Dithering and half-toning algorithm: Dithering and half-toning includes of the following -

  • Error diffusion algorithm
  • Floyd–Steinberg dithering algorithm
  • Ordered dithering algorithm
  • Riemersma dithering algorithm

Elser difference-map algorithm: It is a search algorithm used for general constraint satisfaction problems. It was used initially for X-Ray diffraction microscopy.

Feature detection algorithm: Feature detection consists of -

  • Marr–Hildreth algorithm: It is an early edge detection algorithm
  • Canny edge detector algorithm: Canny edge detector is used for detecting a wide range of edges in images.
  • Generalized Hough transform algorithm
  • Hough transform algorithm
  • SIFT (Scale-invariant feature transform) algorithm: SIFT is an algorithm to identify and define local features in images.
  • SURF (Speeded Up Robust Features) algorithm: SURF is a robust local feature detector.

Richardson–Lucy deconvolution algorithm: This is an image deblurring algorithm.

Blind deconvolution algorithm: Much like Richardson–Lucy deconvolution, it is an image de-blurring algorithm when point spread function is unknown.

Seam carving algorithm: Seam carving algorithm is a content-aware image resizing algorithm

Segmentation algorithm: This particular algorithm parts a digital image into two or more regions.

  • GrowCut algorithm: an interactive segmentation algorithm
  • Random walker algorithm
  • Region growing algorithm
  • Watershed transformation algorithm: A class of algorithms based on the watershed analogy

It is to note down that apart from the algorithms mentioned above, industries also create customized algorithms to address their needs. They can be either right from the scratch or a combination of various algorithmic functions. It is safe to say that with the evolution of computer technology, image processing algorithms have provided sufficient opportunities for multiple researchers and developers for investigation, classification, characterization, and analysis of various hordes of images.