# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 3 Results
### Benchmark Evaluations
#### 3.2.2 Validation of Non-Reference Datasets  
To further validate the superiority of ZLEN, we replicated the existing advanced zero-reference and semi-reference methods and processed low-light images from non-reference low-light datasets, including NPE (84 images), LIME (10 images), MEF (17 images), and DICM (64 images). For each image, we presented the enhancement results on-screen, providing the input image as a reference. To quantitatively assess the subjective visual quality of the different methods, we conducted a user study involving 20 human subjects who independently evaluated the visual quality of the enhanced images. The assessment criteria included observations on whether the enhanced image exhibited under/over-enhanced regions or under/over-exposed artifacts, the presence of color shifts, and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Method** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **LPIPS**\(\downarrow\) \\ \hline \multirow{8}{*}{With} & KinD (ACM ICM, 2019) [40] & 18.970 & 0.782 & 0.257 \\  & KinD++ (IJCV, 2021) [38] & 20.870 & 0.804 & 0.175 \\  & LLFlow (AAAI, 2022) [9] & 24.999 & 0.870 & 0.117 \\  & LLFlow-SKF (CVPR, 2023) [39] & **26.798** & **0.879** & **0.105** \\  & LIME (TIP, 2016) [17] & 16.760 & 0.560 & 0.350 \\  & DRBN (CVPR, 2020) [28] & 19.137 & 0.784 & 0.252 \\  & EnlightenGAN (TIP, 2021) [10] & 17.483 & 0.652 & 0.322 \\ \hline \multirow{2}{*}{Zero} & RetinexNet (BMVC, 2018) [31] & 16.770 & 0.462 & 0.474 \\  & Zero-DCE (CVPR, 2020) [13] & 14.861 & 0.562 & 0.335 \\ \cline{1-1}  & Ours & 19.457 (best) & 0.786 (best) & 0.235 (best) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison with the SOTA methods on the LOL dataset.  
Figure 4: Our method visually compared with the SOTA methods on the LOL test data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Runtime/Second** & **Platform** \\ \hline RetinexNet (BMVC, 2018) [31] & 0.1700 & TensorFlow 2.0 (GPU) \\ EnlightenGAN (TIP, 2021) [10] & 0.0125 & PyTorch 1.8.1 (GPU) \\ Zero-DCE (CVPR, 2020) [13] & 0.0037 & PyTorch 1.8.1 (GPU) \\ Ours & 0.0052 & PyTorch 1.8.1 (GPU) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of running time between non-full reference method and our method.  
existence of noticeable noise and unnatural textures. Visual quality scores, between 1 and 5 (indicating the worst to the best quality), were assigned by the subjects. The average subjective scores are presented for each image set in Table 3, and both the user study (US) and the non-reference perceptual index [16] (PI) are listed to assess the perceptual quality. The PI metric, which was initially employed for measuring perceptual quality in image super-resolution, has gradually become the standard for evaluating the performance of image-enhancement and image-restoration tasks.  
In Table 3, lower PI scores indicate higher perceptual quality, higher US scores indicate better human subjective visual quality, and the bold indicates the score with the best results. From the results in Table 3, our method had better visual and perceptual quality compared with the advanced non-reference methods.