Custom Dataset-Driven Unsupervised Low-Light Image Enhancement Using 2D CNN

Low-light image augmentation, which seeks to improve image visibility and quality under low illumination, is an important job in computer vision. We investigate underexposure image improvement in this study using a variety of datasets, including three established datasets (LOL, ExDark, SICE) and our custom dataset, AR. By merging these datasets, we produced a unique dataset. We propose a novel method for training a robust model using both standard and custom datasets to optimize performance under poor lighting conditions. To guide model training, we analyze five important loss functions: loss of exposure, spatial consistency loss, color constancy loss, illumination smoothness loss, and total loss. These losses improve the image in low-light conditions while ensuring the retention of significant visual elements. spatial consistency loss Additionally, we have made our dataset publicly accessible to facilitate further research. According to experimental results, our model outperforms prior benchmarks and produces encouraging results in terms of improving image quality. Our approach and the AR dataset offer significant resources to the low-light image improvement community, establishing the groundwork for further developments.