Rectifying inhomogeneous illumination in digital images using a latent light manifestation algorithm

Abstract

ABSTRACT Rectification of illumination is vital in enhancing the visibility of images acquired in suboptimal lighting conditions. Still, it remains a challenging yet intriguing process as many existing algorithms fail to meet expectations. Hence, a latent light manifestation (LLM) algorithm is introduced, beginning with a conversion to the HSV space, processing the V channel using the Naka-Rushton and exponential equations. Next, the two output images are blended using a two-step range-expansion blending approach. After that, a statistics-based tonality adjustment method and a dynamic range extension process are applied. Finally, a transfer to the RGB space is performed to produce the output image. The LLM is tested using three datasets, compared with eight contemporary algorithms, and the quality evaluations are carried out via five performance measures. The results denote that the developed LLM consistently outperforms existing algorithms, scoring the best according to the used measures and yielding visually pleasing results.