Invertible Diffusion Models for Compressed Sensing: Efficient End-to-End CS Method

Conceptos Básicos

Efficiently train end-to-end diffusion models for improved image reconstruction in compressed sensing.

Resumen

The article introduces Invertible Diffusion Models (IDM) as an efficient, end-to-end diffusion-based method for compressed sensing. IDM repurposes a large-scale diffusion sampling process to recover original images directly from CS measurements. The paper details the challenges faced by existing methods and proposes solutions to enhance performance while reducing memory consumption. IDM outperforms state-of-the-art CS networks and achieves significant gains in PSNR and inference speed.

Introduction

Challenges in traditional CS methods.
Proposal of Invertible Diffusion Models (IDM).

Related Work

Overview of deep end-to-end learned image CS networks.
Diffusion model-based image reconstruction techniques.

Proposed Method

Detailed explanation of IDM architecture and design choices.
Two-level invertible design for memory efficiency.
Injecting measurement physics into the noise estimator.

Experiment

Comparison with existing methods on various benchmarks.
Evaluation of IDM variants with different sampling steps.

Ablation Study and Analysis

Impact of end-to-end DDNM sampling learning on performance.

Data Extraction

"Experiments demonstrate that IDM outperforms existing state-of-the-art CS networks by up to 2.64dB in PSNR."
"Our method unlocks the power of pre-trained diffusion models to improve CS performance."

Quotations

"We propose IDM, an efficient, end-to-end diffusion-based CS method."
"Our contributions are: We propose IDM, an efficient, end-to-end diffusion-based CS method."

Estadísticas

Experiments demonstrate that IDM outperforms existing state-of-the-art CS networks by up to 2.64dB in PSNR.
Our method unlocks the power of pre-trained diffusion models to improve CS performance.

Citas

"We propose IDM, an efficient, end-to-end diffusion-based CS method."
"Our contributions are: We propose IDM, an efficient, end-to-end diffusion-based CS method."

Ideas clave extraídas de

Invertible Diffusion Models for Compressed Sensing

by Bin Chen,Zhe... a las arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.17006.pdf

Consultas más profundas

How can the proposed two-level invertible design be applied to other neural network architectures

The proposed two-level invertible design can be applied to other neural network architectures by following a similar approach of introducing auxiliary connections and weighting scalars to make the network layers invertible. This design allows for efficient memory management during training by clearing intermediate features in the forward pass and recalculating them during back-propagation. By incorporating this technique into different architectures, researchers can reduce GPU memory usage and improve training efficiency, making it feasible to train large models on standard consumer-grade GPUs.

What are the potential limitations or drawbacks of using injectors to integrate measurement physics into the noise estimator

While injectors offer a way to integrate measurement physics into the noise estimator and enhance reconstruction quality, there are potential limitations or drawbacks associated with their use. One limitation is that injectors may introduce additional complexity to the model architecture, potentially leading to overfitting if not carefully designed or implemented. Moreover, determining the optimal fusion of measurement information with deep features through injectors may require extensive hyperparameter tuning, which could increase computational costs and training time. Additionally, if not properly regularized or controlled, injectors might inadvertently amplify noise or irrelevant information from measurements, affecting the overall performance of the reconstruction process.

How might the findings of this study impact future developments in image reconstruction technologies

The findings of this study have significant implications for future developments in image reconstruction technologies. The introduction of Invertible Diffusion Models (IDM) with an end-to-end diffusion sampling learning framework showcases a novel approach that improves performance while reducing required step numbers and inference times significantly compared to existing methods. This advancement opens up possibilities for more efficient utilization of pre-trained models in various applications beyond compressed sensing.
Incorporating two-level invertibility techniques and lightweight modules like injectors can lead to enhanced adaptability and performance improvements in image reconstruction tasks across different domains such as medical imaging, remote sensing, computer vision applications like object detection or segmentation where high-quality reconstructions from limited data are crucial. Researchers can leverage these insights to develop more efficient algorithms for real-world scenarios requiring accurate image recovery from incomplete measurements while optimizing memory usage and computational resources effectively.