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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.
For example, if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images.
All pixels of a particular value in the original image must be transformed to just one value in the output image. Exact histogram matching is the problem of finding a transformation for a discrete image so that its histogram exactly matches the specified histogram. [4] Several techniques have been proposed for this.
Contourlet transform double filter bank. The contourlet transform uses a double filter bank structure to get the smooth contours of images. In this double filter bank, the Laplacian pyramid (LP) is first used to capture the point discontinuities, and then a directional filter bank (DFB) is used to form those point discontinuities into linear structures.
According to statistics on Open Hub, [31] there is a total of 95 contributors and almost 402,000 lines of code (this include many libraries upon which OTB is built). OTB in also use for the development of the operational ground segment for the VENμS (Vegetation and Environment monitoring on a New Micro Satellite) and the ESA Sentinel-2 missions.
Image rectification is a transformation process used to project images onto a common image plane. This process has several degrees of freedom and there are many strategies for transforming images to the common plane.
A major drawback to application of the algorithm is an inherent reduction in overall image contrast produced by the operation. [1] When utilized for image enhancement, the difference of Gaussians algorithm is typically applied when the size ratio of kernel (2) to kernel (1) is 4:1 or 5:1.
2 triangles, example to show how fractal compression works. Fractal compression is a lossy compression method for digital images, based on fractals.The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image. [1]