Adaptive-neighborhood histogram equalization for image enhancement

Assessing the Impacts of Land Use-Cover Change on Hydrology of Melka Kuntrie Subbasin in Ethiopia, Using a Conceptual Hydrological Model

“…Ice, snow and clouds are white or light cyan [23]. For image, enhancement and classification the most common nonlinear Histogram Equalize Stretch and Supervised Maximum likelihood Classification were used, respectively [24][25][26][27][28]. Thematic image accuracy has been also evaluated how well the class name on the map correspond to what is really on the ground [29,30].…”

Review paper Image Algebra for Electron Images

“…We define a template t such that This is another example of a parametrized template, since its values depend on a parameter, in this case the grey level of image a at x. Then h = tzar EB 1 will be the histogram of a, where 1 denotes a uniform image all of whose grey levels are unity; we see immediately that ho) = 03A3xt(a)x(k) is the number of pixels with the value a(x) = k. The histogram is then used to map the original grey levels into new grey levels according to the various schemes that have been proposed for this purpose [22,45].…”

Histogram Equalization using Neighborhood Metrics

“…Caselles et al [3] propose a local histogram contrast enhancement algorithm which preserves the level sets of an image. Paranjape et al [10] generate adaptive neighborhoods for each pixel by differentiating foreground and background pixels within the neighborhood and using only the foreground pixels to build the local histograms. Dale-Jones and Tjahjadi [6] describe a method in which the window size for building local histograms is varied over the image depending on local image characteristics.…”