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

Abstract

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).

Enhancement of Lightness Image Based on DCP with Color Restoration and Lightness Mapping

“…In the future, the suggested method can be used to enhance retinal images. Ren et al [12] Daway et al [9], Gupta& Agrawal [13] Abraham et al [10]…”

“…This technology was developed to enhance the lightness and color contrast in the image, this algorithm was also applied to the lightness component without changing the color component, using only the YIQ color space, it succeeded in improving lighting and contrast, but it did not recover color information well. Abraham et al [10] proposed a method based on the development of the BLLA algorithm. This method includes improving the lightness based on the BLLA algorithm and using the YIQ color model, they can improve the lightness part by using the sigmoid function without affecting the components of the color, this method succeeded in improving very low-light images and recovering color and lighting information Without any color distortion.…”