Enhancement of Images with Very Low Light by Using Modified Brightness Low Lightness Areas Algorithm Based on Sigmoid Function

Enhancement of images with very low light has become an important role in the field of digital image processing, especially during night photography, tracking and medical imaging using binoculars. In this study, a new algorithm was proposed to enhance images with very low light on the basis of the development of brightness low lightness areas algorithm with the treatment of lighting component (Y) by using Sigmoid function in accordance with YIQ colour space. The proposed method was compared with several algorithms as (contrast enhancement approach, multi-scale retinax with color restoration, histogram equalization, fuzzy logic based-on sigmoid membership function, second-order Taylor series approximation and parallel nonlinear adaptive enhancement) by using non-reference quality measures on the basis of LIME data. Results showed the success of the proposed method on improving images with very low light, obtaining the best quality values rates of Entropy (6.81), NIQE (3.46) and PIQE (35.87).