Histogram Equalization of Grayscale Images

In image processing, several crucial techniques can be applied for grayscale image enhancement. One fundamental method is histogram equalization, which enhances image contrast by redistributing gray-level intensities across the entire dynamic range. This algorithm typically involves calculating the cumulative distribution function of the original histogram and mapping pixel values to achieve a more uniform distribution.

Another technique is gradient enhancement, which emphasizes edges and fine details by computing intensity variations using operators like Sobel or Prewitt filters. This approach typically involves convolution operations with specific kernels to detect horizontal and vertical gradients.

Histogram specification (or histogram matching) is another advanced method that transforms an image's gray-level distribution to match a target histogram pattern. This technique requires calculating transformation functions for both source and target images, then applying a mapping function to align the distributions.

Additionally, direct grayscale transformation can enhance contrast through pixel-wise operations like gamma correction or contrast stretching. These methods involve applying mathematical functions (e.g., power-law transformations) directly to pixel values to adjust brightness and contrast parameters programmatically.