Thresholding (image processing): Difference between revisions

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=== Global vs local thresholding ===

In most methods, the same threshold is applied to all pixels of an image. However, in some cases, it can be advantageous to apply a different threshold to different parts of the image, based on the local value of the pixels. This category of methods is called local or adaptive thresholding. They are particularly adapted to cases where images have inhomogeneous lighting, such as in the sudoku image on the right. In those cases, a neighborhood is defined and a threshold is computed for each pixel and its neighborhood. Many global thresholding methods can be adapted to work in a local way, but there are also methods developed specifically for local thresholding, such as the Niblack<ref>{{Cite book |title=An introduction to digital image processing |date=1986 |publisher=Prentice-Hall International |isbn=0-13-480600-X |oclc=1244113797 }}{{pn|date=April 2024}}</ref> or the Bernsen algorithms.

Software such as [[ImageJ]] propose a wide range of automatic threshold methods, both [https://imagej.net/plugins/auto-threshold global] and [https://imagej.net/plugins/auto-local-threshold local].

=== Benefits of Local Thresholding Over Global Thresholding<ref>Zhou, Huiyu., Wu, Jiahua., Zhang, Jianguo. Digital Image Processing: Part II. United States: Ventus Publishing, 2010.{{pn|date=April 2024}}</ref> ===

=== Benefits of Local Thresholding Over Global Thresholding<ref>Zhou, Huiyu., Wu, Jiahua., Zhang, Jianguo. Digital Image Processing: Part II. United States: Ventus Publishing, 2010.{{pn}}</ref> ===

* Adaptability to Local Image Characteristics: Local thresholding can adapt to variations in illumination, contrast, and texture within different parts of the image. This adaptability helps in handling images with non-uniform lighting conditions or complex textures.

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=== Examples of Algorithms for Local Thresholding ===

* Niblack's Method:<ref>{{Cite book |first=Wayne |last=Niblack |title=An introduction to digital image processing |date=1986 |publisher=Prentice-Hall International |isbn=0-13-480600-X |oclc=1244113797 }}{{pn|pages=115-116 }}</ref> Niblack's algorithm computes a local threshold for each pixel based on the mean and standard deviation of the pixel's neighborhood. It adjusts the threshold based on the local characteristics of the image, making it suitable for handling variations in illumination.

* Bernsen's Method:<ref>Chaki, Nabendu., Shaikh, Soharab Hossain., Saeed, Khalid. Exploring Image Binarization Techniques. Germany: Springer India, 2014.{{pn|date=April 2024}}</ref> Bernsen's algorithm calculates the threshold for each pixel by considering the local contrast within a neighborhood. It uses a fixed window size and is robust to noise and variations in background intensity.

* Sauvola's Method:<ref>{{cite journal |last1=Sauvola |first1=J. |last2=Pietikäinen |first2=M. |title=Adaptive document image binarization |journal=Pattern Recognition |date=February 2000 |volume=33 |issue=2 |pages=225–236 |doi=10.1016/S0031-3203(99)00055-2 |bibcode=2000PatRe..33..225S }}</ref> Sauvola's algorithm extends Niblack's method by incorporating a dynamic factor that adapts the threshold based on the local contrast and mean intensity. This adaptive factor improves the binarization results, particularly in regions with varying contrasts.