## 2 Related Work
### Low-Light Image Enhancement  
Low-light image enhancement is to enhance the brightness and details of an image to obtain a color-saturated image with clear details. In recent years, more and more low-illumination image enhancement algorithms have been proposed. [PERSON] [6] proposed the Retinex model, which treats each image as the result of the product of the illumination component R and the reflection component L. After that, many Retinex-based methods were proposed, such as MSR [8], SSR [7], SRIE [15], etc., which were also used for image enhancement. However, due to the incomplete separation of the illumination and reflection components, the enhanced images often suffer from edge enhancement overload and color distortion, resulting in visual degradation. Based on Retinex theory, also widely used in other image enhancement, the underwater image enhancement method [16] uses a color correction scheme to eliminate chromatic aberrations, then by imposing a multiorder gradient prior on the reflectance and illumination maps, and finally, establishes a maximum a posteriori formula for the target image on the color-corrected image. IB (Illumination Boost  
Figure 2: Overall structure of EFCANet. CAFM is Cross-Attention Fusion Module, and XAF is cross-attention fusion. CAF is Channel Attention Fusion. algorithm) [17] targets the enhancement of nighttime images, and the algorithm applies logarithmic and exponential functions to enhance local contrast, uses the LIP method to obtain the features of both images, uses S-curves to improve the overall brightness, and uses linear scaling functions to resize pixels. Intensity is readjusted to the standard dynamic range. The IB algorithm keeps the well-exposed areas of the original image from being enlarged and generates an image with better perceptual quality. Noise may be amplified during the image enhancement process. Therefore, image enhancement should be performed to preserve details and remove noise at the same time. With the development of deep learning, some excellent traditional algorithms and theories have been introduced to deep learning for mapping low-light images to normal-light images. In the RetinexNet [1] algorithm, RetinexNet uses Decom-Net constraint to learn the smooth map of reflection and illumination, Enhance-Net to enhance the illumination map, and a joint denoising method to remove the noise from the reflection map; this algorithm can effectively enhance the detail information. The KinD (Kindling the Darkness) [2] algorithm, also inspired by Retinex theory, decouples the original image into two components, which are then responsible for adjusting the light and removing degradation to achieve better regularized learning. Recently, RetinexDIP [18] proposed a new Retinex decomposition \"generation\" strategy to transform the decomposition problem into a generation problem.The DLN [19] algorithm treats low-light image enhancement as a problem of estimating the residuals between low-light and normal-light images. The GLADNet [20] algorithm uses a network of encoders and decoders to generate the global prior information of the illumination map. The global prior information is reconstructed with the original image to reconstruct the detail information. GLADNet uses a nearest-neighbor interpolation operation and is able to avoid object vignetting. EnlightenGAN (EnGAN) [3] can be trained in the absence of low/normal-light image pairs, using an attention-guided U-Net as a generator, using a double discriminator to guide the global and local information. Self-feature retention loss is used to guide the training process. DRBN [21] enhances images in two phases, the first phase learns to recover image details on a paired dataset, and the second phase uses a GAN network to improve the visual quality of the image. Zero-DCE [22] and Zero-DCE++ [4] are unsupervised learning networks that treat the image enhancement task as a higher-order curve estimation problem.