TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain (2311.00660v3)

Abstract: Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce artifacts and distortions and struggle to control the amount of rain generated due to a lack of proper constraints. In this paper, we propose an unpaired image-to-image translation framework for generating realistic rainy images. We first introduce a Triangular Probability Similarity (TPS) constraint to guide the generated images toward clear and rainy images in the discriminator manifold, thereby minimizing artifacts and distortions during rain generation. Unlike conventional contrastive learning approaches, which indiscriminately push negative samples away from the anchors, we propose a Semantic Noise Contrastive Estimation (SeNCE) strategy and reassess the pushing force of negative samples based on the semantic similarity between the clear and the rainy images and the feature similarity between the anchor and the negative samples. Experiments demonstrate realistic rain generation with minimal artifacts and distortions, which benefits image deraining and object detection in rain. Furthermore, the method can be used to generate realistic snowy and night images, underscoring its potential for broader applicability. Code is available at https://github.com/ShenZheng2000/TPSeNCE.

• The paper introduces TPSeNCE, a novel framework combining unpaired image-to-image translation with specific constraints to generate realistic, artifact-free rain effects in images.
• TPSeNCE employs Triangular Probability Similarity (TPS) loss to reduce artifacts via geometric constraints and Semantic Noise Contrastive Estimation (SeNCE) to control rain intensity through semantic-guided feature alignment.
• The framework is validated on multiple datasets, showing significant improvements in generating realistic, artifact-free rain effects that enhance performance in image deraining and object detection tasks.

Overview of TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain

The paper, "TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain," addresses the critical challenge of generating realistic rainy images without introducing artifacts and distortions, which is pivotal for tasks such as image deraining and object detection in adverse weather conditions. The authors introduce an innovative framework that combines unpaired image-to-image translation with novel constraints to achieve this objective.

Problem Background

Rain severely affects image quality and visibility, posing significant challenges for automated vision tasks like object detection. The paper recognizes that while rain generation algorithms hold promise for enhancing scene understanding in rainy conditions, existing methods often produce images marred by artifacts and lack precise control over the rain intensity. The research aims to bridge the gap between synthetic and real rainy images, improving generalization for algorithms trained on generated data.

Methodology

The authors propose an unpaired image-to-image translation framework grounded in two novel methodologies:

1. Triangular Probability Similarity (TPS): The TPS loss is devised to minimize artifacts in generated rainy images by incorporating a geometric constraint. It aligns the generated images closer to clear and real rainy images within the discriminator manifold. This approach curtails artifacts and distortions by ensuring that the generated images adhere more closely to the authentic characteristics of both clear and rainy conditions, as evidenced in their analysis of T-SNE visualizations.
2. Semantic Noise Contrastive Estimation (SeNCE): Departing from traditional contrastive learning approaches that uniformly push negative samples away, SeNCE introduces a nuanced method of reweighting this 'pushing force'. It evaluates feature similarity between clear and rainy images while employing semantic segmentation to regulate the feature alignment force. This adjustment allows for a controlled generation of the rain effect, ensuring the output is neither excessively rainy nor unrealistically clear.

Results and Evaluation

The framework is validated across several datasets like BDD100K, INIT, and Boreas. It undergoes rigorous testing for image quality, deraining efficiency, and object detection accuracy. The introduction of TPS and SeNCE shows notable improvements in both quantitative metrics (content preservation, style adherence, KID, FID) and qualitative user studies evaluating artifact severity and realism. The algorithm significantly outperforms existing methodologies in creating artifact-free, realistic rain effects that greatly benefit downstream processing such as deraining and detection in adverse conditions.

Implications and Future Work

Practically, this research has the potential to improve the robustness of autonomous systems operating in rainy conditions. Theoretically, it advances the field by providing new insights into effectively harnessing discriminator manifolds and contrastive learning for weather condition simulation. The expansion of this framework to generate snowy and night images illustrates its broader applicability, offering a versatile approach for varying atmospheric conditions beyond rain.

Future work could explore refining these methods further to handle extreme weather conditions or adaptively adjusting the severity of rain effects based on environmental cues. Additionally, enhancing computational efficiency for faster processing without compromising quality could be a subsequent research avenue.

In sum, the methodologies proposed in this paper offer a significant step forward in synthetic weather condition generation, addressing artifact reduction while providing controllable, realistic outputs for improved computational vision applications.