EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution (2310.19288v1)

Abstract: Recently, convolutional networks have achieved remarkable development in remote sensing image Super-Resoltuion (SR) by minimizing the regression objectives, e.g., MSE loss. However, despite achieving impressive performance, these methods often suffer from poor visual quality with over-smooth issues. Generative adversarial networks have the potential to infer intricate details, but they are easy to collapse, resulting in undesirable artifacts. To mitigate these issues, in this paper, we first introduce Diffusion Probabilistic Model (DPM) for efficient remote sensing image SR, dubbed EDiffSR. EDiffSR is easy to train and maintains the merits of DPM in generating perceptual-pleasant images. Specifically, different from previous works using heavy UNet for noise prediction, we develop an Efficient Activation Network (EANet) to achieve favorable noise prediction performance by simplified channel attention and simple gate operation, which dramatically reduces the computational budget. Moreover, to introduce more valuable prior knowledge into the proposed EDiffSR, a practical Conditional Prior Enhancement Module (CPEM) is developed to help extract an enriched condition. Unlike most DPM-based SR models that directly generate conditions by amplifying LR images, the proposed CPEM helps to retain more informative cues for accurate SR. Extensive experiments on four remote sensing datasets demonstrate that EDiffSR can restore visual-pleasant images on simulated and real-world remote sensing images, both quantitatively and qualitatively. The code of EDiffSR will be available at https://github.com/XY-boy/EDiffSR

• The paper’s main contribution is introducing EDiffSR, which combines a lightweight architecture with enhanced conditioning to overcome over-smoothing and instability in image super-resolution.
• It employs an Efficient Activation Network (EANet) and a Conditional Prior Enhancement Module (CPEM) to achieve high-fidelity outputs with lower computational cost.
• Experimental results on multiple datasets show superior FID, NIQE, and AG scores, confirming its robustness and potential for broader image restoration applications.

EDiffSR: Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

The paper introduces EDiffSR, a novel Diffusion Probabilistic Model (DPM) designed for efficient super-resolution (SR) of remote sensing images. While traditional methods, including convolutional and generative adversarial networks, have shown potential, they often grapple with issues such as over-smoothing and training instability. EDiffSR aims to address these limitations by leveraging the strengths of DPMs, which are renowned for their ability to produce perceptually pleasing, complex data distributions while maintaining stable training dynamics.

Key Innovations

EDiffSR distinguishes itself through several core innovations:

1. Efficient Activation Network (EANet): Diverging from the commonly used heavy UNet in DPM-based noise prediction, EANet offers a lightweight alternative featuring efficient architectural choices like simplified channel attention and simple gate operations. This design enables favorable performance in noise prediction with lower computational demands.
2. Conditional Prior Enhancement Module (CPEM): This module enhances the prior knowledge input to the diffusion model. By extracting enriched conditional inputs from lower-resolution images, CPEM aids in capturing more informative cues, unlike existing methodologies that typically rely on rudimentary bicubic upsampled conditions.
3. Integration with Stochastic Differential Equations (SDE): To expedite the sampling process, EDiffSR employs SDEs, facilitating a more computationally efficient diffusion reversal procedure.

Experimental Evaluation

The efficacy of EDiffSR is validated across multiple mainstream remote sensing datasets (AID, DOTA, DIOR, and NWPU-RESISC45). The empirical results, covering both qualitative and quantitative measures, position EDiffSR as a leading approach in DPM-based SR models. Notable gains were observed in generating high-fidelity and visually satisfactory SR outputs, evidenced by superior FID scores across varied datasets.

Additionally, EDiffSR excels not only in synthetic scenarios but also in real-world application settings. It achieves commendable NIQE and AG performance on the NWPU-RESISC45 dataset, indicative of its generalizability and robustness against diverse remote sensing environments.

Implications and Future Work

The methodological framework and experimental validation of EDiffSR offer several implications for future academic and practical pursuits:

1. Lightweight Efficient Architectures: EDiffSR sets a benchmark for leveraging lightweight neural architectures in diffusion models, challenging the conventional belief that larger models necessarily yield superior reconstruction results.

2. Adaptive Prior Utilization: CPEM's success points toward a promising direction for prior enhancement techniques in SR problems. It underscores the potential for further exploration of conditional input refinement that could improve SR performance in other domains.

3. DPM Versatility and Expansion: While EDiffSR focuses on SR, the underlying principles can be generalized to other image restoration tasks. The exploration of DPMs in diverse application domains, particularly those requiring high-resolution imagery, could yield fruitful research avenues.

In conclusion, EDiffSR's combination of an efficient architecture with an enriched conditioning approach exemplifies significant progress in the domain of image super-resolution for remote sensing. Its results offer a compelling proposition for adopting diffusion probabilistic models in both academic research and practical image processing applications.