# Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving
## 5 Results and Discussion  
Figure 3 shows the input images, target images, and predicted images obtained using our proposed method. The target images and the predicted images are close in detail and contrast, and their subjective visual effects are similar. Table 1 shows the evaluation metrics, including contrast per pixel (CPP) [28], mean pixel contrast (MPC) [29], enhancement measure evaluation (EME) [30], image clarity (IC) [31], and entropy (E) [28]. Their formulas have the following representation:  
\[CPP=\frac{1}{(H-1)(W-1)}\sum_{x}\sum_{y}\frac{|G(x,y)|}{\sqrt{2}} \tag{16}\]  
\[MPC=\frac{C_{processed}}{C_{Original}} \tag{17}\]  
\[EME_{\alpha,x,y}(\phi)=\frac{1}{xy}\sum_{l=1}^{y}\sum_{k=1}^{y}20 ln\frac{l^{ w}_{max,k,l}(\phi.par)}{l^{w}_{min,k,l}(\phi.par)+c} \tag{18}\]  
\[IC=\sigma_{RGYB}+(0.3\mu_{RGYB}) \tag{19}\]  
\[E=-\sum_{i=0}^{l-1}P(q)\cdot\log(P(q)) \tag{20}\]  
In Equation (1), \(H\) and \(W\) represent the size of the image, and \(G(x,y)\) is the gradient vector of the image. In Equation (2), \(C\) is the average contrast. \(C_{processed}\) and \(C_{Original}\) are the contrast of the input image and the processed image, respectively. For Equation (3), the image is broken up into \(x,y\) blocks, \(\phi\) is the given transform, and \(\alpha\) is an enhancement parameter. \(C\) is a constant value of 0.0001. In Equation (4), \(\sigma\) represents the standard deviation and \(\mu\) is the mean value of all pixels. In Equation (5), \(E\) represents the set of image pixel values, \(q\) is the pixel of the image, and \(P(q)\) represents the probability that one pixel value will appear. For the first input image, the predicted results and the target image only differ significantly in the EME evaluation index, and are close in other aspects. For the second and third input images, there are large differences between the predicted and target images on EME  
Figure 3: Input images, target images, and predicted images for road scenes. and CPP, and all other aspects are close. For the fourth input image, the predicted result and the target image only have a large difference on CPP, and the other aspects are close to each other. The results show that the predicted results are very close to the target images in terms of details. However, there is still room for improvement in our approach to EME and CPP. Figure 4 shows the input images and enhanced images of our method and other algorithms. We selected four representative images for a comprehensive comparison, including sequences with multiple targets, no targets, and mixed targets. For the first image, the HE method makes the roof of the car on the road too dark and other parts of the car too bright. Although the SSR [32] and MSR methods do not make the car appear obviously locally too bright or too dark, they make the lines on the road unclear. Our method not only avoids partial over-brightening or over-darkening of the cars, but also maintains the details of the lines on the road. For the second image, the HE method makes the sky darker, resulting in some clouds not being easy to notice. The images processed using the SSR and MSR methods have more clouds, but the details on the road are still unclear. The use of our method to enhance the image can not only clearly exhibit the clouds in the sky, but also preserve the details on the road. For the third image, the HE method makes the car and the building on the right appear too bright, and the SSR and MSR methods make some textures of the building blurred, so their images are not as detailed as those of the HE method. Our method not only avoids the local over-brightness caused by the HE method, but also makes the details of the building clearer than the SSR and MSR methods. For the fourth image, the HE method not only makes the tires of the car appear too bright, but also gives the trees above part of the image similar brightness to the night sky, which lowers the contrast between trees and the night sky.