Image Filtering and Enhancement

Contrast adjustment, morphological filtering, deblurring, ROI-based processing

Image enhancement is the process of adjusting images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or adjust the contrast of an image, making it easier to identify key features.

Image Filtering
Convolution and correlation, predefined and custom filters, nonlinear filtering, edge-preserving filters
Contrast Adjustment
Contrast adjustment, histogram equalization, decorrelation stretching
Morphological Operations
Dilate, erode, reconstruct, and perform other morphological operations
Deblurring
Deconvolution for deblurring
ROI-Based Processing
Define and operate on regions of interest (ROI)
Neighborhood and Block Processing
Define neighborhoods and blocks for filtering and I/O operations
Image Arithmetic
Add, subtract, multiply, and divide images

Featured Examples

Contrast Enhancement Techniques

Adjust the contrast of grayscale and color images using three techniques: intensity value mapping, histogram equalization, and contrast-limited adaptive histogram equalization.

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Comparison of Auto White Balance Algorithms

Our eyes are very good at judging what is white under different lighting conditions. Digital cameras, however, without some kind of adjustment, can easily capture unrealistic images with a strong color cast. Automatic white balance (AWB) algorithms try to correct for the ambient light with minimum input from the user, so that the resulting image looks like what our eyes would see. But which is the best AWB algorithm to use? In this example, we explain the process behind auto white balance and show how to compare and select the best algorithm.

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