# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 7 Methods for Image Enhancement  
Severe weather conditions, such as rain and snow, adversely may affect the visual quality of the images acquired under such conditions; thus, rendering them useless for further usage and sharing. In addition, such degraded images usually drastically affect the performance of vision systems. Mainly, it is essential to address the problem of single image de-raining. However, the inherent ill-posed nature of the situation presents several challenges. In [107], it is proposed an image de-raining conditional generative adversarial network (ID-CGAN) that account for quantitative, visual, and also discriminative performance into the objective function. The proposal method explores the capabilities of conditional generative adversarial networks (CGAN), in combination with additional constraint to enforce the de-rained image to be indistinguishable from its corresponding GT clean image. A refined loss function and other architectural novelties in the generator-discriminator pair were also introduced, with the loss function aimed towards the reduction of artifacts introduced by GAN, ensuring better visual quality. The generator sub-network is constructed using densely connected networks, whereas the discriminator is designed to leverage global and local information and between real/fake images. Exhaustive experiments were conducted against several State-of-the-Art (SOTA) methods using synthetic datasets derived from the UCID [108] and BSD-500 [109] datasets, and with external noise artifacts added. The experiments were evaluated on synthetic and real images using several evaluation metrics such as peak signal to noise ratio (PSNR), structural similarity index (SSIM) [110], universal quality index (UQI) [111], and visual information fidelity (VIF) [112], with the proposed model achieving an PSNR (DB) of 24.34. Moreover, experimental results evaluated on object detection methods, such as FasterRCNN [94], demonstrated the effectiveness of the proposed method in improving the detection performance on images degraded by rain. A single-image-based rain removal framework was proposed in [113] by properly formulating the rain removal problem as an image decomposition problem based on the morphological decomposition analysis. The alternative to applying a conventional image decomposition technique, the proposed method first decomposes an image into the low and high-frequency (HF) components by employing a bilateral filter. The HF part is then decomposed into a \"rain component\" and a \"non-rain component\" using sparse coding. The model experiments were conducted on synthetic rain images built using an image software, with the model achieving a VIF of 0.60. While the method has some degree of \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Cat** & **Ref.** & **Main Technique(s)** & **\# Data** & **Pros/Cons** \\ \hline \multirow{4}{*}{[102]} & \multirow{2}{*}{[105]} & 3D reconstruction, & KITTI & Robust, \\  & & 2D optical flow & MOTA 0.848 & Short term ID handled \\ \hline \multirow{4}{*}{[105]} & CNN features, & - & Robust, \\  & Kalman + Association & - & Short term ID handled \\ \hline \hline \end{tabular}  
[105]  
\end{table} 
Table 10: Performance evaluation of the reviewed vehicle ReID Tracking methods. performance with common rain conditions, it has difficulties to handling more complex rain dynamic scenarios. In [114], an effective method based on simple patch-based priors for both the background and rain layers is proposed, which is based on the Gaussian mixture model (GMM) to accommodate multiple orientations and scales of the rain streaks. The two GMMs for the background and rain layers, defined as GB and GR, are based on a pre-trained GMM model with 200 mixture components. The method was evaluated using synthetic and real images, and the results compared to SOTA methods, with the proposed method achieving an SSIM of 0.88. In [115], it is proposed a DNN architecture called DerainNet for removing rain streaks from an image.