# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Comparison with State-of-the-Art Methods  
In order to fully evaluate the effectiveness and advancement of the method proposed in this paper, the comparison experiments were conducted with other advanced low-light image enhancement methods, including Retinexformer [32], SMG [33], LEDNet [25], SNR [34], Zero-DCE++[19], and EnlightenGAN [35]. Retinexformer designs a lighting-guided Transformer that uses lighting representation to model non-local interactions. This framework restores damage by estimating lighting information to achieve the purpose of enhancing low-light images. SMG is a new framework for low-light image enhancement that simultaneously models appearance and structure. It uses structural features to guide appearance enhancement, resulting in precise and realistic results. LEDNet combines low-light enhancement and deblurring operations and uses adaptive skip connections to associate the synergy between the encoder and decoder. It introduces pyramid pooling modules and filters adaptive convolution in the encoder and decoder. It simultaneously implements lighting enhancement and deblurring operations on images and releases the public dataset LOL-Blur. SNR is a novel solution for low-light image enhancement that collectively exploits signal-to-noise ratio-aware transformers and convolutional models to dynamically enhance pixels with spatial-varying operations. Computing an SNR is proposed before guiding the feature fusion and formulating the SNRaware transformer with a new self-attention model to avoid tokens from noisy image regions of very low SNR. [PERSON] et al. [19] proposed the Zero-DCE++ algorithm, based on the original network structure Zero-DCE, in which depth-separable convolution is used to replace the traditional convolution, and the input image is down-sampled to estimate the curve and then up-sampled. Sampling recovery enhances the image, the improved model's parameter amount and calculation amount are significantly reduced, and the model's inference speed is improved. [PERSON] et al. [35] proposed the unsupervised algorithm EnlightenGAN, which uses the U-Net network as the generator, and adopts a global-local dual discriminator structure and a self-regularization perceptual loss function to ensure the enhanced image is closer to the real image. Figure 5 shows a visual comparison of the low-light enhancement effect of our method and other comparison methods on an image in the LOL-Blur test set. Figure 6 shows the results of the LOL-V1 test data. It can be seen that the image brightness reconstructed by ZeroDCE and EnlightenGAN methods is still very dark, and the color is distorted. The images reconstructed by Retinexformer, SMG, and SNR methods have color distortion and overexposure problems, and the content is blurry. The brightness of the image reconstructed using the LEDNet method is close to the results of our proposed method. In summary, the image reconstructed by our proposed method has apparent brightness and content clarity advantages. In addition to the above subjective evaluation, this section also compares the results of our proposed method and other methods on objective evaluation indicators. Table 1 shows the results of the comparison of the three evaluation indicators of PSNR, SSIM, and LPIPS with the LACCNet proposed in this paper and the comparisons with the LOL-Blur and LOL-V1 test sets. It can be seen from the results that, compared with the SOTA methods, the LACCNet method proposed in this paper has the best overall effect on the LOL-Blur dataset. PSNR reached 26.728, an increase of 0.998, and LPIPS was 0.199, a decrease of 0.035. LACCNet's results on the LOL-V1 dataset have also reached a more advanced level. However, the results show that LACCNet performs better on the LOL-Blur dataset, which has more severe blur problems. The result may reflect our addition of the Deblur Decoder Block to LACCNet to make it more suitable for blurred scenes. Figure 5: Comparison of visualization effects of different low-light image enhancement methods on the LOL-Blur test set.