This paper presents two novel recursive partitioned one-to-one gray level mapping (RPOGM) algorithms, viz., recursive median partitioned one-to-one gray level mapping (RMDPOGM) and recursive mean partitioned one-to-one gray level mapping (RMPOGM). The proposed RPOGM methods serve multiple objectives and address the issues such as (i) intensity saturation, (ii) intensity compression and (iii) ensure uniform degree of enhancement of all gray levels and thus result in overall enhancement of the processed image. In RMPOGM, image/histogram is partitioned recursively, (recursion level restricted to two, resulting in four sub-histograms) based on mean. RMDPOGM is similar to RMPOGM except histogram partitioning is done based on median. In RPOGM methods, image-dependent weights for each sub-histogram are calculated separately. Later, these weights are used for transformation. Finally, all the transformed sub-images are combined to get the processed image. As the images processed by these methods are not having any loss of details, it results in retaining the structural details of the objects and hence preserves fine contours even after enhancement. This results in low gradient magnitude similarity deviation (GMSD) between the processed image and input image. Experimental results show the superiority of the proposed methods over the state-of-the-art histogram equalization methods in terms of preserving entropy, preserving mean brightness and having low GMSD. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.