# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 4 Experiments
### Quantitative Results on Synthetic Images  
We adopt the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) [51] as image quality indicators for quantitative analysis. PSNR is generally used to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **ReLU** & **Tanh** & **Sigmoid** & **BCRelu** \\ \hline PSNR (SOTS) & 24.59 & 20.07 & 18.61 & **24.63** \\ \hline SSIM (SOTS) & 0.904 & 0.901 & 0.859 & **0.905** \\ \hline PSNR (ITS) & 25.31 & 23.97 & 22.21 & **25.62** \\ \hline SSIM (ITS) & 0.905 & 0.924 & 0.902 & **0.925** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results of quality evaluation indicators on SOTS and ITS datasets using different activation functions in the final layer. Figure 4: Bilateral constrained rectifier linear unit(BCReLU). measure the reference value of image quality between the maximum signal and background noise, and the larger the value, the lower the image distortion. PSNR can be expressed as  
\[\text{PSNR}=10\times\log_{10}(\frac{(\text{MaxValue})^{2}}{\text{MSE}}), \tag{10}\]  
where MSE is the mean square error of two images, and MaxValue is the maximum value that can be obtained from image pixels. SSIM is an indicator that measures the similarity between two images. From the perspective of image composition, SSIM defines structural information as independent of brightness and contrast, reflecting the properties of object structures in the scene. SSIM models distortion as a combination of three different factors: brightness, contrast, and structure. It uses the mean as the estimate of brightness, standard deviation as the estimate of contrast, and covariance as the measure of structural similarity. \[\text{SSIM}(x,y)=\frac{(2u_{x}u_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(u_{x}^{2}+u_{ y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})}, \tag{11}\]  
where \(C_{1}=(K_{1}L)^{2}\) and \(C_{2}=(K_{2}L)^{2}\) are used to avoid situations where the denominator is 0; \(L\) is equivalent to MaxValue in PSNR, which is a very small constant; \(u_{x}\) and \(u_{y}\) are the mean; \(\sigma_{x}^{2}\) is the standard deviation; and \(\sigma_{xy}\) is the variance. Compared to PSNR, SSIM is more in line with human visual characteristics in evaluating image quality. High PSNR and SSIM scores indicate low image distortion and a more similar structure. We compare IFE-Net with the powerful methods in recent years based on PSNR and SSIM indicators. DCP [11] does not require precise physical modeling of haze in images but only relies on the prior principle of dark channels to reliably calculate the transmission matrix for image dehazing. Dehaze-Net [31] is an end-to-end system that utilizes prior knowledge to obtain atmospheric light, only learns the medium transmission map through the network, and ultimately obtains clean images. AOD-Net [21] is the first end-to-end trainable dehazing model, which does not separately estimate the parameters in Equation (1) but rather unifies all parameters into one and directly obtains a clean image from the hazy image. FFA-Net [36] has a feature fusion attention mechanism, and the design of the network allows it to perform well with dense hazes, textures, and details. GCA-Net [52] applies gated subnetworks and smooth extended convolutions, which is beneficial for fusing features of different scales and removing possible grid artifacts. DWGAN [53] introduces 2D discrete wavelet transform, aiming at restoring clear texture details and retaining sufficient high-frequency information. GUNet [54] significantly reduces overhead while effectively removing haze. The images in the RESIDE dataset [55] were selected for experimental evaluation of our method. Figure 5 shows the dehazing results of some randomly selected synthetic images from the SOTS datasets. DCP [11], Dehaze-Net [14], and DWGAN [53] successfully remove heavy haze, but they exhibit color distortion and increased brightness.