Enhancing Underwater Images with Image-to-Image Diffusion

Table of Contents

1. Introduction
2. The Concept of Domain Transfer in Underwater Image Enhancement
3. Understanding the Challenges in Underwater Computer Vision
4. Existing Approaches: GANs, Physics-based Models, and 3D Underwater Modeling
5. Introducing Image-to-Image Diffusion Model
6. The Role of Stable Diffusion in Reducing Dependence on Image Structure
7. Using CLIP Embeddings to Enhance Output Accuracy
8. The Proposed Diffusion Pipeline
9. Collecting and Using Different Datasets
10. Evaluating the Accuracy of the Model
11. Metrics: PSNR, SSIM, UCIQE, and UIQM
12. Baseline Images and Future Improvements
13. Conclusion and Future Directions
14. Acknowledgments
15. References

😃 Introduction

In the field of underwater computer vision, the concept of domain transfer plays a crucial role in enhancing underwater images. This process involves adapting computer vision models to effectively work in challenging underwater domains that differ from the usual above-water domains. The underwater environment presents unique challenges, such as distortion, light attenuation, turbidity, poor illumination, and color diminishing, making it difficult to capture clear images. To overcome these challenges, our research focuses on using an image-to-image diffusion model for domain transfer, specifically converting underwater images to above-water images.

✨ The Concept of Domain Transfer in Underwater Image Enhancement

The domain transfer process aims to address the limitations of computer vision models when working with underwater images. While techniques such as ResNets, Faster R-CNN, segmentation, and NERF work well with above-water domains, they struggle to perform effectively underwater due to distortion and light attenuation issues. This limitation hinders downstream applications and the analysis of underwater images. To solve this challenge, our research focuses on domain transfer, which involves adapting models to work effectively in diverse and challenging underwater domains. By transferring the domain from underwater to above-water images, we can produce better results and enable more accurate analysis.

🌊 Understanding the Challenges in Underwater Computer Vision

Underwater computer vision has various applications, including fish tracking, ocean resource exploration, marine ecology research, naval military applications, and biological monitoring. However, the underwater environment presents several challenges that hinder clear image capture. These challenges include light attenuation, turbidity, poor illumination, and color diminishing. As a result, it becomes harder to Collect clear images from underwater environments. These limitations highlight the need for image enhancement techniques that can improve the quality of underwater images.

📚 Existing Approaches: GANs, Physics-based Models, and 3D Underwater Modeling

Various approaches have been explored to address the domain transfer problem in underwater computer vision. Some of these approaches include Generative Adversarial Networks (GANs), which use adversarial training to generate realistic underwater images. Other models are based on physics principles, attempting to simulate the distortion of light through an underwater environment. Additionally, some approaches involve 3D underwater modeling, aiming to create a comprehensive model of the entire underwater environment. However, these existing models often lack generalizability, adaptability, and can lead to Artifact formation during the denoising process.

🖼️ Introducing Image-to-Image Diffusion Model

The image-to-image diffusion model is a powerful tool we utilize in our research for domain transfer in underwater image enhancement. This model consists of two processes: forward diffusion and reverse diffusion. During forward diffusion, noise is iteratively added to an image until it becomes completely noisy. In contrast, reverse diffusion trains the model to iteratively remove the noise, resulting in a denoised image. The model consists of convolutional layers that are interconnected to form a unit. This unit decreases the image size in a hyperparabolic Shape, and the layers on opposite sides are connected, as depicted in the Diagram.

🔍 The Role of Stable Diffusion in Reducing Dependence on Image Structure

To reduce dependence on the structure of images and focus more on the scene's structure, we utilize a pre-trained Stable Diffusion Model. This pre-trained model has a general understanding of above-water imagery, allowing us to leverage its knowledge during the domain transfer process. Our model takes in the underwater image and a noisy version of the corresponding above-water image as input. It conditions on the underwater image and the CLIP embedding of the underwater image. By incorporating stable diffusion, we can enhance the transferability of the model and generate more accurate output images.

🌟 Using CLIP Embeddings to Enhance Output Accuracy

CLIP (Contrastive Language-Image Pretraining) is an open AI model that understands the association between images and text descriptions in a shared latent space. We harness the power of CLIP embeddings to guide our model towards generating output images that Resemble both the original above-water image and the characteristics of the underwater image. By conditioning our model on CLIP embeddings, we ensure that the final image aligns with the original image while incorporating the necessary details and features from the underwater domain. CLIP embeddings improve the accuracy of our model's output and enhance the domain transfer process.

⚙️ The Proposed Diffusion Pipeline

Our proposed diffusion pipeline involves performing forward diffusion on an above-water image by iteratively adding Gaussian noise. This step is followed by reverse diffusion, where our model denoises the image while conditioning on the underwater image and its CLIP embedding. By combining these diffusion processes, we can effectively transfer the underwater domain to the above-water domain and generate high-quality, denoised images. The utilization of stable diffusion, along with CLIP embeddings, ensures that the output accurately represents the underwater scene while maintaining the details and characteristics of above-water images.

📊 Collecting and Using Different Datasets

To train our model and evaluate its effectiveness, we collect data from three different datasets: UIEB, EUVP, and U45. These datasets provide a diverse range of underwater images, allowing us to capture various aspects of underwater domains. By using multiple datasets, we increase the diversity of our training data, enhancing the generalizability of our model. The images collected from these datasets serve as reference points for our training process, enabling our model to learn and adapt to different underwater environments.

📏 Evaluating the Accuracy of the Model

We measure the accuracy of our model using several metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Underwater Color Image Quality Evaluation (UCIQE), and Underwater Image Quality Measure (UIQM). PSNR and SSIM are standard metrics used to evaluate image quality and fidelity. UCIQE and UIQM are specific to underwater computer vision and help quantify the artifacts caused by underwater distortion, such as color cast, blur, and contrast. These metrics provide insights into the performance of our model and are crucial for comparing it with other underwater enhancement models.

📊 Baseline Images and Future Improvements

As our model is still under training due to computational constraints, we provide baseline images created using existing models such as Hugging Face's and Struck Pixapix. These baseline images showcase the limitations and potential for improvement in existing models. We anticipate that our final model will produce significantly higher quality and fidelity results compared to the baseline. In the future, we aim to reduce the computational cost for real-time enhancement, fine-tune the model to distinguish between noise and image details, and decrease the dependency on the quality of target images.

🔮 Conclusion and Future Directions

In conclusion, our research focuses on using image-to-image diffusion for underwater image enhancement. By leveraging the concept of domain transfer and incorporating stable diffusion and CLIP embeddings, we aim to advance the field of underwater computer vision. Our model aims to produce accurate and high-quality output images that preserve the characteristics of the underwater scene while enhancing their visual appeal. Moving forward, we plan to conduct ablation tests to further evaluate the effectiveness of our CLIP embedding loss function and explore real-time enhancement and fine-tuning techniques to improve our model's performance.

🙏 Acknowledgments

We would like to express our gratitude to Blast AI for their invaluable support and resources, which have been instrumental in conducting this research. Their expertise and guidance have greatly contributed to the success of our project.

📚 References

• Reference 1
• Reference 2
• Reference 3
• Reference 4
• Reference 5

Highlights

• The concept of domain transfer in underwater image enhancement
• Challenges in underwater computer vision: distortion, light attenuation, turbidity, poor illumination, and color diminishing
• Image-to-image diffusion model: forward and reverse diffusion processes
• Reducing dependence on image structure using stable diffusion
• Enhancing accuracy with CLIP embeddings
• Proposed diffusion pipeline: forward and reverse diffusion with underwater conditioning
• Collecting and using different datasets: UIEB, EUVP, and U45
• Evaluating accuracy with PSNR, SSIM, UCIQE, and UIQM metrics
• Baseline images and future improvements
• Advancing the field of underwater computer vision through domain transfer and image enhancement techniques

FAQ

Q: What is domain transfer in underwater computer vision? A: Domain transfer involves adapting computer vision models to effectively work in diverse and challenging underwater domains, enabling the enhancement and analysis of underwater images.

Q: How does the image-to-image diffusion model work? A: The model consists of forward and reverse diffusion processes. Forward diffusion adds noise iteratively to an image, while reverse diffusion denoises the image, conditioning on the underwater image and CLIP embeddings.

Q: What datasets are used in the research? A: The research utilizes the UIEB, EUVP, and U45 datasets to capture a wide range of underwater images and enhance the generalizability of the model.

Q: How is the accuracy of the model evaluated? A: The accuracy is measured using metrics such as PSNR, SSIM, UCIQE, and UIQM, which assess the quality, fidelity, and artifacts of the output images.

Q: What are the future directions of the research? A: The research aims to improve real-time enhancement, fine-tune the model for noise and image detail distinction, and reduce the dependency on target image quality.