Abstract:
Image taken in difficult lighting situations from dim settings to extremely dark scenes still are
a recurring problem in modern photography. Specifically, this research addresses the complex
problem of low-light picture restoration, including overexposure in bright regions,
and inadequate dark image restoration. Using a Generative Adversarial Network (GAN) model
to address all lighting-complexity-related problems and produce an overall solution is the
primary aim.
The method used is training a GAN model to understand and correct specifics related to varying
degrees of low-light conditions. The model is trained on a wide range of data that includes
images with varying brightness across specific regions, extremely dark images, and slightly
dim images. Through adversarial training, the GAN can find the best enhancements so that
brilliant areas are recovered without being overexposed, poorly lit sections are brought back
into suitable visibility, and very dark areas are properly illuminated.
Following 100 epochs of training on lighter datasets, each comprising fewer than 600 image
pairs, the LLMEV model demonstrates the ability to restore low-light images, achieving mean
output scores of 21.58 dB for Peak Signal-to-Noise Ratio (PSNR) and 0.885 for Structural
Similarity Index (SSIM).
