Self-Prompt Dehazing Transformers with Depth-Consistency for Image Restoration

Core Concepts

Effective depth-consistency self-prompt Transformer for image dehazing.

Abstract

This work introduces a novel approach to image dehazing using a self-prompt Transformer that focuses on depth-consistency. By leveraging the differences in estimated depths between hazy images and their clear counterparts, the model aims to guide deep learning models for better restoration. The method involves generating prompts based on depth differences, embedding prompts to perceive haze residuals, and utilizing prompt attention for improved haze removal. Continuous self-prompt inference is proposed to iteratively correct deep models towards generating better haze-free images. Experimental results demonstrate superior performance in perception metrics compared to state-of-the-art approaches on synthetic and real-world datasets.

Stats

Extensive experiments show method outperforms state-of-the-art approaches on both synthetic and real-world datasets. Perception metrics including NIQE, PI, and PIQE are used for evaluation. Model achieves better perception quality in terms of NIQE, PI, and PIQE.

Quotes

Key Insights Distilled From

SelfPromer

by Cong Wang,Ji... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2303.07033.pdf

Deeper Inquiries

How does the continuous self-prompt inference compare to recurrent dehazing methods

The continuous self-prompt inference approach in image dehazing outperforms recurrent dehazing methods. The continuous self-prompt method progressively improves the dehazing quality by iteratively correcting the deep models towards better haze-free image generation. In contrast, recurrent dehazing methods may not achieve similar or better results as they lack the progressive correction mechanism that the continuous self-prompt approach offers. This iterative nature of the continuous self-prompt allows for a more refined and accurate removal of haze residuals over multiple steps, leading to superior results compared to recurrent approaches.

What are the limitations of the proposed method in handling different types of haze

One limitation of the proposed method is its dependence on significant depth differences between images with haze residuals and their clear counterparts for effective performance. If these depth differences are not pronounced enough, it may impact the model's ability to generate high-quality dehazed images consistently. Additionally, slight variations in depth estimation between hazy images and clear ones could lead to performance degradation in scenarios where subtle haze residuals exist but are not effectively captured by the model.

How can the model's performance be further improved for real-world applications beyond image dehazing

To further enhance the model's performance for real-world applications beyond image dehazing, several strategies can be implemented: Robustness Testing: Conduct extensive testing under diverse real-world conditions to ensure that the model performs well across various environments and lighting conditions. Data Augmentation: Incorporate a wide range of real-world data during training to improve generalization capabilities and adaptability to unseen scenarios. Transfer Learning: Utilize transfer learning techniques to fine-tune pre-trained models on specific real-world datasets for improved performance on practical applications. Integration with Sensor Data: Integrate sensor data such as LiDAR or radar inputs into the model architecture to enhance spatial awareness and improve accuracy in complex real-world settings. Feedback Mechanisms: Implement feedback loops or reinforcement learning mechanisms that allow the model to learn from its own predictions and continuously improve based on user feedback or ground truth information from real-world scenarios. By incorporating these strategies, the model can be optimized for robust performance in challenging real-world applications beyond traditional image dehazing tasks.