Low-light image enhancement using the illumination boost algorithm along with the SKWGIF method

Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main problems, the first of which is low visibility (i.e., small pixel intensities). Secondly, due to the low signal-to-noise ratio, noise also becomes prominent and obscures the image content. For this reason, images with low noise are usually employed in this application, a practice not possible in the real world. In this regard, a hybrid method is proposed which is based on the illumination boost algorithm (IBA) and weighted guided image filtering with steering kernel (SKWGIF). IBA is used to raise the values of low and medium intensity pixels while preventing excessive increases in high-value pixels. SKWGIF is employed to denoise and augment image details by adjusting its parameters based on the output of the previous step. The proposed method utilizes the LOL v1, LOL v2 and ExDark datasets Our hybrid method’s comprehensive approach to low-light image enhancement produced better outcomes than previous methods. Quantitative results have shown that the proposed model outperforms state-of-the-art (SOTA) models on the LOL v1 dataset, achieving a PSNR value of 18.80. Through the successful resolution of visibility and noise reduction issues, our approach led to notable gains in image quality measures, including SSIM, PSNR, BRISQUE, and NIQE. IBA’s ability to selectively boost intensity improved image visibility without producing overexposure artifacts, and SKWGIF effectively reduced noise while enhancing image details. The combined effect produced improved image quality that was superior to that of single techniques or already-used hybrid approaches.