Correcting Low-Illumination Images Using Multi-Scale Fusion in a Pyramidal Framework

In this work, the authors have proposed a method for improving the visual quality of color images suffering from low illumination. In this pyramid based multiscale technique, the input image is decomposed into four different levels of resolutions. Starting from the coarsest resolution, each image is converted to HSV space, and the illumination is computed from the V component employing multiscale Gaussian function. At this stage, Weber-Fechner law is used to construct two enhanced versions of the illumination component corresponding to two different values of a parameter. These two images are fused using PCA to construct a new V image, which is subsequently super-sampled to its next higher level of resolution. The V images of the next higher level of resolution are subjected to the same treatment until we reach the base level of the pyramid. Finally, all the V component images at the base level are fused employing PCA to construct the final resultant V component, which in turn is combined with the H and S components to construct the final result. The method has been implemented and tested on a set of real 2D color images, and the results are found satisfactory. The experimental results have been compared with those of other methods based on some objective measures.