# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 4 Experiments  
### Experimental Implementation Details  
The method adopted is trained and tested on an NVIDIA GeForce RTX2080 graphics card, combined with an Intel i7-8700 processor and 32G of RAM. The software environment employed Pycharm version 2022, equipped with the Anaconda Python 3.7 interpreter and the Pytorch 1.12.1 framework. The initial learning rate was set to 0.001, with \(\lambda_{1}\) and \(\lambda_{2}\) both set to 0.1 in Equation (1), \(\lambda_{3}\) and \(\lambda_{4}\) set to 0.01 in Equation (2), \(\lambda_{5}\) set to 0.01 in Equation (5), and the remaining weights set to 1.  
The training dataset utilized in this study comprises the LOL dataset [5] and a partially synthesized dataset. The total number of images included in the LOL dataset was 500 pairs. Out of these pairs, 485 were used as training sets, and 15 were used as test sets. This is re garded as the most commonly used image enhancement dataset in the research community. For the synthetic version of the dataset, we collected about 1000 images to compensate for the low number of LOL files. The LOL dataset is obtained by capturing images using a camera with varying exposure times and ISO sensitivity. The dataset primarily consists of indoor images with a resolution of 400 \(\times\) 600. The synthetic dataset is generated by processing the RAISE image dataset using Adobe Lightroom. The images in this dataset are unprocessed high-resolution RAW images captured by four photographers over a period of three years using three cameras at different locations across Europe, capturing various scenes at different times.  
We compare quantitatively with traditional methods Retinex, MSRCP, and deep-learning-based methods such as MBLLEN [15], lightnet [16], RetinexNet [5], EnlightenGAN [17], RRDNet [14], DSLR [18], ExCNet [19], Zero-DCE, Zero-DCE++ [20], and R2 RNet. For the testing dataset, we used the paired LOL dataset, VE-LOL-H dataset, and unpaired LIME dataset. We used reference-based evaluation metrics including peak signal-to-noise ratio (PSNR) [21], structural similarity (SSIM) [21], mean squared error (MSE), learned perceptual image patch similarity (LPIPS), and variance inflation factor (VIF) [22], as well as reference-free natural image quality evaluation metrics such as the Natural Image Quality Evaluator [23] to evaluate the deep-learning-based methods.  
PSNR is a supervised evaluation method that measures the quality of an image's reconstruction. According to the evaluation, the closer two images are to one another, the better the reconstruction's quality.  
The structural similarity of an image is measured by using SSIM, which takes into account the differences in structure, contrast, and brightness between different images. Values range from zero to 1, which indicates a better-quality image.  
MSE is an image evaluation metric that calculates the average of the squared differences between image pixels and reference image pixels. A smaller MSE value indicates that the image quality is closer to that of the reference image.  
LPIPS is a statistical measure of the perceptual similarity of two images. It is commonly used to evaluate the human eye's perception of images. Compared to other indices, the LPIPS is closely related to the subjective evaluation of humans.  
VIF is an index that combines the statistical and natural data collected by the visual system and the human eye to evaluate the quality of images. It shows a correlation between the two values and the images' perceived visual quality.  
NIQE is a non-reference evaluation metric. Both the PSNR and SSIM datasets are paired, but we need non-reference evaluation metrics for non-paired image datasets. Furthermore, it has been found that the PSNR and SSIM metrics do not apply to image texture detail, and NIQE can effectively bridge this gap. The smaller the value of NIQE, the better the image quality.