Deep Joint Rain Detection and Removal from a Single Image (1609.07769v3)

Abstract: In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in the new rain image models and a novel deep learning architecture. We first modify an existing model comprising a rain streak layer and a background layer, by adding a binary map that locates rain streak regions. Second, we create a new model consisting of a component representing rain streak accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which usually happen in heavy rain. Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. To handle rain streak accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose a recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively. In each recurrence of our method, a new contextualized dilated network is developed to exploit regional contextual information and outputs better representation for rain detection. The evaluation on real images, particularly on heavy rain, shows the effectiveness of our novel models and architecture, outperforming the state-of-the-art methods significantly. Our codes and data sets will be publicly available.

• The paper introduces a multi-task deep learning architecture that jointly learns a binary rain streak map, rain appearance, and clean background to improve rain removal.
• It employs a recurrent network with contextualized dilated convolutions to progressively refine predictions for complex rain streak accumulations.
• Experimental evaluations on datasets like Rain12, Rain100L, and Rain100H, as well as real images, demonstrate significant PSNR and SSIM improvements.

An In-Depth Analysis of "Deep Joint Rain Detection and Removal from a Single Image"

The paper "Deep Joint Rain Detection and Removal from a Single Image," authored by Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan, presents a comprehensive deep learning approach to address the challenging problem of rain removal from a single image. This essay provides an insightful overview of the technical contributions, methodologies, and implications of their work.

Technical Contributions

The authors introduce several key innovations:

1. Enhanced Rain Image Model: The image model is first extended by incorporating a binary map to locate rain streak regions. This model is further advanced to represent rain streak accumulation and different shapes and directions of overlapping streaks, simulating heavy rain effectively.
2. Deep Learning Architecture: A novel multi-task deep learning architecture is proposed. This architecture jointly learns a binary rain streak map, the visible appearance of rain streaks, and the clean background image.
3. Recurrent Network for Progressive Removal: To handle heavy rain, a recurrent network structure is introduced. This network iteratively and progressively clears rain streaks and rain accumulation by leveraging a contextualized dilated network to improve rain detection.
4. Combination of Deraining and Dehazing: For images with rain accumulation, a sequential process combining deraining and dehazing is proposed. This joint approach is designed to address the atmospheric veiling effect caused by dense rain streaks.

Methodology

The core methodology outlined involves several critical stages:

• Rain Streak and Background Separation: The rain image model is redefined by incorporating a binary mask to identify rain streak regions explicitly, thereby enhancing the model’s expressiveness.
• Deep Multi-Task Learning: The paper leverages a multi-task network where the binary mask, rain streak characteristics, and background details are learned jointly. This multi-task framework benefits from additional information obtained from the binary mask, improving rain removal performance.
• Contextualized Dilated Networks: The authors implement a contextualized dilated network to enhance feature extraction by incorporating multiple scales of spatial context while maintaining local detail integrity.
• Recurrent Network Structure: By employing a recurrent processing paradigm, the network progressively refines its predictions at each iteration, effectively handling complex cases of rain streak accumulation and varying streak directions.
• Joint Deraining and Dehazing: The paper proposes a unique sequence of deraining, dehazing, and secondary deraining to address heavy rain scenarios comprehensively.

Experimental Evaluation

The evaluation focuses on both synthesized and real rain images, demonstrating significant improvements over state-of-the-art methods. The contribution is quantitatively validated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index (SSIM), showing notable performance gains.

Results

• Rain12 and Rain100L datasets: For simpler rain streak datasets, the proposed JORDER and JORDER-R methods outperformed others significantly with PSNR and SSIM scores demonstrating superior rain removal capability.
• Rain100H dataset: Handling complex rain streak scenarios, the JORDER-R achieved significant performance gains, highlighting the effectiveness of the recurrent approach.
• Real Images: Qualitative assessments on real images validated the practical utility of the proposed method, particularly in scenarios involving heavy rain and rain streak accumulation.

Implications and Future Directions

This research provides a marked improvement in single-image rain removal, with practical implications for various outdoor computer vision tasks, such as autonomous driving, surveillance, and remote sensing, where visual clarity is crucial under adverse weather conditions.

Future developments could explore:

• Enhancing processing speed and efficiency, making it feasible for real-time applications.
• Extending the model to handle other weather conditions like snow or hail.
• Integrating the approach into broader image restoration frameworks to manage multiple degradation effects simultaneously.

Conclusion

The paper "Deep Joint Rain Detection and Removal from a Single Image" sets a new benchmark in rain removal from single images. By introducing advanced rain image models and leveraging deep learning architectures, it addresses the complex degradations caused by rain effectively. The novel methodologies and substantial empirical results illustrate its robustness and effectiveness, paving the way for future advancements in this domain.