Name
Abstract | ||
|---|---|---|
Rainy weather greatly affects the visibility of salient objects and scenes in the captured images and videos. The object/scene visibility varies with the type of raindrops, i.e. adherent rain droplets, streaks, rain, mist, etc. Moreover, they pose multifaceted challenges to detect and remove the raindrops to reconstruct the rain-free image for higher-level tasks like object detection, road segmentation etc. Recently, both Convolutional Neural Networks (CNN) and Generative Adversarial Network (GAN) based models have been designed to remove rain droplets from a single image by dealing with it as an image to image mapping problem. However, most of them fail to capture the complexities of the task, create blurry output, or are not time efficient. GANs are a prime candidate for solving this problem as they are extremely effective in learning image maps without harsh overfitting. In this paper, we design a simple yet effective 'DerainGAN' framework to achieve improved deraining performance over the existing state-of-the-art methods. The learning is based on a Wasserstein GAN and perceptual loss incorporated into the architecture. We empirically analyze the effect of different parameter choices to train the model for better optimization. We also identify the strengths and limitations of various components for single image deraining by performing multiple ablation studies on our model. The robustness of the proposed method is evaluated over two synthetic and one real-world rainy image datasets using Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. The proposed DerainGAN significantly outperforms almost all state-of-the-art models in Rain100L and Rain700 datasets, both in semantic and visual appearance, achieving SSIM of 0.8201 and PSNR 24.15 in Rain700 and SSIM of 0.8701 and PSNR of 28.30 in Rain100L. This accounts for an average improvement of 10 percent in PSNR and 20 percent in SSIM over benchmarked methods. Moreover, the DerainGAN is one of the fastest methods in terms of time taken to process the image, giving it over 0.1 to 150 seconds of advantage in some cases. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-021-11442-6 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Image deraining, Generative Adversarial Networks, Deep learning, Wasserstein Loss, Perceptual Loss | Journal | 80 |
Issue | ISSN | Citations |
30 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sahil Yadav | 1 | 0 | 0.34 |
| Aryan Mehra | 2 | 0 | 0.34 |
| Honnesh Rohmetra | 3 | 0 | 0.34 |
| Rahul Ratnakumar | 4 | 0 | 0.34 |
| Pratik Narang | 5 | 60 | 11.31 |