Fuzzified Histogram Equalization based Gamma Corrected Cosine Transformed Energy Redistribution for Image Enhancement

A non-iterative fuzzy-based approach is proposed here, for image quality enhancement by processing image in histogram-domain followed by subsequent processing in the cosine-transformed domain. This method employs gamma correction through an adaptively derived gamma value-set, which is just an association of different gamma values for individual intensity levels. For proper evaluation of highly adaptive gamma value-set, the fuzzy derived input histogram followed by the content adaptive thresholding is applied and followed by fuzzified histogram’s sub-equalization. Subsequently, its cumulative distribution inspired adaptive gamma value set is derived for interim intensity channel improvement. In addition to this, two-dimensional (2-D) discrete cosine transformation (DCT) based decomposition for gamma corrected channel in accordance with energy dependent image texture improvement framework is effectively suggested. In other words, the core approach proposal can also be understood an excellence achieved by dealing up the trade-off among fuzzified smoothing based information harvesting along with DCT based textural amplification by magnifying the lower energy coefficients by maintain the image adaptive thresholding. The experimental results derived here, effectively advocates the excellence of the proposed framework when compared in context of the state-of-the-art methodologies.