Recovering Irradiance from Images Corrupted by Atmospheric Turbulence using Diffeomorphic Template Registration

Core Concepts

A method for recovering the underlying irradiance from a collection of images corrupted by atmospheric turbulence, by modeling the deformation using optical flow and a novel flow inversion algorithm, without requiring an explicit initialization of the reference template.

Abstract

The paper presents a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. The key insights are:

Rather than initializing a latent irradiance ("template") by heuristics, the method selects one of the input images as a reference and models the deformation in this image by the aggregation of the optical flow from it to other images, exploiting a prior imposed by the Central Limit Theorem.

With a novel flow inversion module, the model registers each image to the template without the template, avoiding artifacts related to poor template initialization.

The method uses the simplest optical flow (Horn-Schunck) yet achieves state-of-the-art performance on atmospheric turbulence mitigation benchmarks. It establishes a strong baseline that can be further improved by integrating it into more sophisticated pipelines.

The method models the blur caused by atmospheric turbulence as a Gaussian kernel and solves a blind deconvolution problem to recover the final irradiance.

Experiments show the method outperforms existing model-based and learning-based approaches in terms of PSNR and SSIM, while being computationally efficient.

Stats

The paper presents the following key metrics and figures:

The method achieves state-of-the-art PSNR of 20.74 and SSIM of 0.789 on the HeatChamber dataset.
On the CLEAR-sim dataset, the method achieves PSNR of 25.95 and SSIM of 0.906, outperforming both NDIR and TurbRecon.
The processing time for 100 frames of size 220x220 is 31 seconds, which is significantly faster than the 30 minutes required by NDIR.

Quotes

"Rather than initializing a latent irradiance ("template") by heuristics to estimate deformation, we select one of the images as a reference, and model the deformation in this image by the aggregation of the optical flow from it to other images, exploiting a prior imposed by Central Limit Theorem."
"With a novel flow inversion module, the model registers each image TO the template but WITHOUT the template, avoiding artifacts related to poor template initialization."

Key Insights Distilled From

Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation

by Dong Lao,Con... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03662.pdf

Deeper Inquiries

How can the optical flow estimation be further improved to better handle dynamic scenes with moving objects

To improve optical flow estimation for dynamic scenes with moving objects, several strategies can be implemented:

Adaptive Flow Resolution: Implementing an adaptive flow resolution mechanism can help capture finer details in regions with high motion while maintaining efficiency in regions with less motion. By dynamically adjusting the flow resolution based on the local motion complexity, the optical flow estimation can better handle dynamic scenes.

Motion Segmentation: Prior segmentation of the scene into regions with different motion characteristics can help tailor the optical flow estimation process. By treating each segment separately, the optical flow can be optimized to capture specific types of motion, such as rigid body motion or deformable motion.

Temporal Consistency: Incorporating temporal consistency constraints can enhance the robustness of optical flow estimation in dynamic scenes. By enforcing smoothness over time and ensuring consistency between consecutive frames, the optical flow can better handle the temporal evolution of motion in the scene.

Object Detection and Tracking: Integrating object detection and tracking algorithms can provide additional cues for optical flow estimation. By leveraging object trajectories and dynamics, the optical flow can be guided to focus on relevant regions of interest, improving accuracy in the presence of moving objects.

What other priors or regularizers could be incorporated to the blind deconvolution step to further enhance the reconstruction quality

Incorporating additional priors or regularizers into the blind deconvolution step can further enhance the reconstruction quality:

Sparse Gradient Regularization: By promoting sparsity in the gradient domain, the blind deconvolution process can prioritize sharp edges and textures, leading to crisper reconstructions. Sparse gradient regularization can help suppress noise and artifacts while preserving important image details.

Non-local Similarity Constraints: Introducing non-local similarity constraints can improve the consistency of reconstructed regions across frames. By encouraging similarity between corresponding patches in different frames, the blind deconvolution process can produce more coherent and visually pleasing results.

Structural Priors: Leveraging structural priors, such as edge continuity and texture coherence, can guide the blind deconvolution process towards generating more realistic reconstructions. By enforcing structural constraints based on the expected characteristics of the scene, the reconstruction quality can be significantly enhanced.

Adaptive Regularization Strength: Implementing an adaptive regularization strength mechanism based on local image characteristics can optimize the trade-off between fidelity and regularization. By dynamically adjusting the regularization parameters, the blind deconvolution process can adapt to varying levels of noise and blur in different image regions.

Can the proposed method be extended to handle other types of image degradation beyond atmospheric turbulence, such as motion blur or sensor noise

The proposed method can be extended to handle other types of image degradation beyond atmospheric turbulence, such as motion blur or sensor noise, by incorporating specific adaptations:

Motion Blur Handling: To address motion blur, the method can integrate motion estimation techniques to compensate for the blur introduced by object motion during image capture. By estimating the motion blur kernel and deconvolving it from the image, the method can effectively mitigate motion blur artifacts.

Sensor Noise Reduction: For handling sensor noise, the method can incorporate denoising algorithms as a preprocessing step before the blind deconvolution process. By reducing noise levels in the input images, the blind deconvolution step can focus on enhancing image details rather than amplifying noise.

Combined Degradation Models: To tackle complex degradation scenarios, the method can incorporate combined degradation models that account for multiple factors simultaneously. By integrating modules for handling different types of degradation, such as blur, noise, and distortion, the method can provide comprehensive image restoration solutions for diverse real-world scenarios.