# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Quantitative Evaluation  
Since SSIM has already been described, here, we briefly describe the PSNR evaluator as follows. PSNR (Peak Signal-to-Noise Ratio) is one of the most prevalent and widely adopted objective evaluation metrics for image quality assessment. A higher PSNR value signifies a lower level of noise interference and distortion within the image, reflecting improved fidelity and clarity. This metric can be mathematically formulated as follows:  
\[PSNR=10\cdot\lg\frac{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\{\max[f(i,j)] \}^{2}}{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}[f^{\prime}(i,j)-f(i,j)]^{2}}, \tag{19}\]  
where \(M\) represents the height of the image, \(N\) represents the width of the image, \(\max[f(i,j)]\) represents the maximum pixel value of the image, and \(f(i,j)\) denotes the pixel value at the row \(i\) and column \(j\) of the image. PIQE is a no-reference image quality assessment metric that leverages perceptual features to evaluate image quality by mimicking the perception mechanisms of the human visual system. It specifically calculates the image quality score by analyzing the block structure and noise characteristics of the image. A lower PIQE score indicates superior image quality. BRISQUE is a no-reference image quality evaluation metric that relies on natural scene statistics. By extracting histogram data, local features, contrast, texture, and edge information from the image, it accurately and stably identifies distortion effects that affect human visual perception. A lower BRISQUE score signifies a more natural appearance of the image, thereby indicating higher image quality. The final experimental results, presented in Tables 1-5, demonstrate the superior performance of the proposed algorithm across all evaluated metrics. Although it slightly lags behind the Retinexformer algorithm in the PIQE metric, the Retinexformer significantly underperforms the proposed algorithm in other metrics, suggesting potential over-processing during enhancement, resulting in the loss of image details and a certain degree of distortion. In terms of processing speed, unsupervised deep learning algorithms generally exhibit faster performances, whereas the proposed algorithm is comparable to the CLIP-LIT algorithm, completing the processing of a single image within an average of 0.5 s. In contrast, the proposed algorithm demonstrates significant advantages across all metrics, with notably higher PSNR and SSIM values. This not only validates the effectiveness of our algorithm in enhancing image contrast and brightness but also highlights its ability to preserve low distortion levels while enhancing the dark details in low-light images, achieving a balance between computational efficiency and enhancement performance. These results fully underscore the efficiency and practicality of our algorithm in processing low-light images. Based on the comprehensive results shown in Figures 9-15 and Tables 1-5, the proposed algorithm performs excellently in both subjective and objective evaluations. It enhances the brightness and contrast of low-light images, highlights dark details, and effectively suppresses color distortion, resulting in images with better visibility.