## 3 Proposed Method
### DLIE Module  
Typically, image correction and enhancement operations involve manual adjustment of filter parameters based on experience, which poses several challenges. Firstly, manual adjustment is prone to subjective factors and experience limitations, leading to subjective parameter choices and significant errors. Secondly, parameters adjusted manually are often optimized for specific scenes or image collections, lacking generality across different scenes and diverse images, thereby limiting the algorithm's applicability. Thirdly, manually tuned parameters may not adapt well to changes in different environments and image conditions, resulting in poor system adaptability when facing new data. To address these challenges, this study proposes an automated method using a small CNN network to estimate filter parameters. This approach improves the performance and applicability of image correction and enhancement operations by enabling a more comprehensive search of the parameter space. It reduces subjective errors, enhances the system's adaptability, and achieves better results across different scenes and image conditions. The DLIE module comprised both pixel-level filters and sharpening filters. The pixel-level filter involved four adjustable parameters: white balance, gamma correction, hue adjustment, and contrast enhancement. Its primary purpose was to smooth the image post-dehazing, deraining, and desnowing to improve visual quality. Table 1 presents descriptions of the four filters and their parameters. The white balance (WB) filter achieved white balance by adjusting the weights (\(W_{r}\), \(W_{g}\), \(W_{b}\)) of the red \(r_{i}\), green \(g_{i}\), and blue \(b_{i}\) channels of the input image. The output \(X_{o}\) was composed of the weighted sum of the input channels. The gamma filter adjusted the image by multiplying each pixel value \(X_{i}\) by the parameter \(G\). The contrast filter adjusted the image contrast by varying the brightness values, where \(\alpha\) is a parameter ranging between 0 and 1. The tone filter adjusted the image hue by applying different hue functions (\(L_{t_{r}}\), \(L_{t_{g}}\), \(L_{t_{h}}\)) to each color channel. The parameter \(t_{i}\) represents the hue function. The parameters for the contrast filter mapping function were defined as follows: Equation (7) computed the brightness value \(L(X_{i})\) of the input pixel by applying a weighted sum to the three channels. \(\omega_{1},\ \omega_{2},\ \omega_{3}\) were adjustable weight parameters for the corresponding channels. Equation (8) adjusted the brightness non-linearly using a cosine transformation of the brightness value, thus producing enhanced contrast \(MaL(X_{i})\). Equation (9) adjusted the entire pixel by multiplying the input pixel value \(X_{i}\) with the enhanced brightness value \(MaL(X_{i})\) to the brightness value \(L(X_{i})\). \[L(X_{i})=\omega_{1}r_{i}+\omega_{2}g_{i}+\omega_{3}b_{i} \tag{7}\]  
\[MaL(X_{i})=\frac{1}{2}(1-cos(\alpha\times(L(X_{i})))) \tag{8}\]  
\[Ma(X_{i})=X_{i}\times\frac{MaL(X_{i})}{L(X_{i})} \tag{9}\]  
By processing the contrast parameters as described above, the contrast filter mapping function became more flexible and suitable for various image scenes. This enabled effective enhancement of image contrast, thereby improving image quality and visual effects. The image sharpening filter primarily utilized image sharpening to compensate for contours and highlight edge information, making the image clearer after haze removal. The process of image sharpening can be described as follows. The primary function of the image sharpening filter was to enhance image clarity by accentuating contours and highlighting edge information, particularly beneficial after haze removal. \[F(x,\lambda)=I(x)+\beta\cdot\lambda\cdot(I(x)-EnhanceFunc(I(x)) \tag{10}\]  
Equation (10) represents a function for image enhancement, and \(EnhanceFunc(I(x))\) is a function used to enhance details.