Restoring Raindrops Using Attentive Generative Adversarial Networks

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

Removing Rain Component from A Color Image

It is well-known that a bad weather, e.g. haze, rain, or snow affects severely the quality of the captured images or videos. Also raindrops adhered to a glass window or camera lens can severely affect the visibility of background scene and degrade the image quality, which consequently degrades the performance of many image processing and computer vision system algorithms. These algorithms are used in various applications such as object detection, tracking, recognition, and surveillance also in navigation. Rain removal from a video or a single image has been an active research topic over the past decade. Today, it continues to draw attentions in outdoor vision systems (e.g. surveillance) where the ultimate goal is to produce a clear and clean image or video. The most critical task here is to separate rain component from the other part. For that purpose, we are proposing an efficient algorithm to remove rain from a color image.

DR-Net: A Novel Generative Adversarial Network for Single Image Deraining

Blurred vision images caused by rainy weather can negatively influence the performance of outdoor vision systems. Therefore, it is necessary to remove rain streaks from single image. In this work, a multiscale generative adversarial network- (GAN-) based model is presented, called DR-Net, for single image deraining. The proposed architecture includes two subnetworks, i.e., generator subnetwork and discriminator subnetwork. We introduce a multiscale generator subnetwork which contains two convolution branches with different kernel sizes, where the smaller one captures the local rain drops information, and the larger one pays close attention to the spatial information. The discriminator subnetwork acts as a supervision signal to promote the generator subnetwork to generate more quality derained image. It is demonstrated that the proposed method yields in relatively higher performance in comparison to other state-of-the-art deraining models in terms of derained image quality and computing efficiency.