# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Qualititative Evaluation  
In this section, we conduct a comprehensive comparison of our method against several state-of-the-art approaches for low-light image enhancement. Our benchmark includes two conventional methods (MSRCR [19], Dong [25]), three supervised methods (KinD++ [33], RetinexNet [31], Retinexformer [32]) and five unsupervised methods (EnlightGAN [36], Zero-DCE++ [41], CLIP-LIT [37], NeRCo [38], Colie [39]). Figure 8 displays the training/testing curves that illustrate the variation in total loss over epochs. These curves mirror the training process of the model across four distinct datasets: SICE, LOLv1 [31], LOLv2-real, and LOLv2-synthetic [51]. It is observable from the figure that, when working on the SICE and LOLv1 datasets, the model converges after approximately 50 training epochs, exhibiting stable training and testing losses. On the LOLv2-real dataset, the model converges more rapidly, achieving convergence after around 25 epochs. Meanwhile, on the LOLv2-synthetic dataset, despite the slower convergence rate, the testing loss gradually decreases during the training process and subsequently stabilizes alongside the training loss, demonstrating strong generalization abilities. In summary, these curves indicate that the model is capable of effective training on different datasets and can swiftly identify optimal or near-optimal solutions. Figures 9-15 show some representative results for visual comparison, drawing from the LOLv1 dataset (500 images), the LOLv2-real dataset (789 images), the LOLv2-synthetic dataset (1000 images), the DICM dataset [52] (42 images), the MEF dataset [53] (16 images), and the NPE dataset [54] (8 images). The LOL dataset captures pairs of low-light and normal-light images by adjusting the camera's exposure time and ISO settings, thereby offering a comprehensive resource for research on image enhancement in low-light conditions. It has v1 and v2 versions. LOL-v2 is divided into real and synthetic subsets. To further validate the robustness of the proposed algorithm, we augment our testing to include extreme scenarios, encompassing overexposure and complexly fluctuating lighting conditions among other challenging settings. We choose to conduct experiments on the ExDark dataset [55], which comprises 7363 images captured under varying degrees of illumination, ranging from extreme darkness to dusk. These images exhibit exceptionally complex lighting conditions, encompassing 10 distinct lighting scenarios, and simulate a wide range of real-world low-light environments, such as night vision and scenes illuminated by faint moonlight. Figure 8: Training/testing curves for total loss epochs on (SICE, LOLv1, LOLv2-real, and LOLv2-synthetic) datasets. Figure 9: Qualitative results of LOLv1 dataset. Figure 11: Qualitative results of LOLv2-synthetic dataset. Figure 10: Qualitative results of LOLv2-real dataset. For traditional algorithms, the MSRCR algorithm [19] significantly enhances image contrast but limits brightening effects, with dark regions remaining dim and even accompanied by color distortion. The Dong algorithm [25] improves image brightness and enhances edge details, but some edges are over-enhanced, making the image unnatural and introducing noticeable block effects that compromise the overall image quality. Among supervised deep learning algorithms, the KinD++ algorithm [33] effectively boosts image brightness and contrast, but introduces excessive noise in dark regions after enhancement, resulting in the loss of edge details. The RetinexNet algorithm [31] produces significant noise and color distortion issues. The Retinexformer algorithm [32] causes obvious overexposure and color distortion in the high-light areas of the image. Among unsupervised deep learning algorithms, the EnlightGAN algorithm [36] enhances the brightness of dark regions while strengthening edge details, but color distortion and over-enhancement in bright regions  
Figure 12: Qualitative results of DICM dataset. Figure 13: Qualitative results of MEF dataset.