Contrast Adjustment Filters

Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image. It is therefore suitable for improving the local contrast and enhancing the definitions of edges in each region of an image. However, AHE has a tendency to overamplify noise in relatively homogeneous regions of an image. A variant of adaptive histogram equalization called contrast limited adaptive histogram equalization (CLAHE) prevents this by limiting the amplification. In the case of CLAHE, the contrast limiting procedure is applied for each neighborhood from which a transformation function is derived.

References

[1] https://en.wikipedia.org/wiki/Adaptive_histogram_equalization

Histogram equalization is a contrast enhancement technique in which the gray scale is remapped so that the output image uses the entire range available and there are approximately the same number of pixels of each gray value in the output image. The equalized image has a roughly linear cumulative distribution function.

You should note that the effectiveness of using the same number of pixels for each gray value must be judged empirically. You should also note that this operation can cause large homogeneous regions to be remapped into more gray levels. This may or may not help in image interpretation.

While histogram equalization has the advantage that it requires no parameters, it sometimes yields unnatural looking images. An alternative method is local histogram equalization.

References

[1] http://en.wikipedia.org/wiki/Histogram_equalization

Enhances images with low contrast by spreading out the most frequent intensity values in an image. While equalized images have a roughly linear cumulative distribution function for each pixel neighborhood, the local version of histogram equalization emphasizes local gray level variations.

References

[1] http://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_local_equalize.html?highlight=local%20histogram%20equalization

Normalization is a process that recalculates the range of pixel values in an image so that it is equal to the maximum range for the data type. For example, 0 to 255 for 8-bit images, 0 to 65535 for 16-bit images, and 0 to 1.0 for float images. This contrast stretch is similar to the Auto Contrast option in Adobe Photoshop and is often referred to as dynamic range expansion. For example, if the intensity range of the image is 50 to 180 and the desired range is 0 to 255 the process entails subtracting 50 from each of pixel intensity, making the range 0 to 130. Then each pixel intensity is multiplied by 255/130, making the range 0 to 255.

The purpose of dynamic range expansion is usually to bring the image, or other type of signal, into a range that is more familiar or normal, hence the term normalization. Often, the motivation is to achieve consistency in dynamic range for a set of data.

The Slope Map filter relies on the blurring effects of a Gaussian filter to quantify underlying local trends characterizing image intensity and balance contrast. For example, it can remove vignetting artefacts quite effectively. It is also equivalent to a local histogram equalization without priors if a global histogram equalization is applied to the slope map afterwards. It typically works well on mammogram and X-ray images.