Dual-Domain Feature Fusion Network for Enhancing Low-Light Remote Sensing Images

Core Concepts

A dual-domain feature fusion network is proposed to effectively decouple the degradation in low-light remote sensing images and progressively restore high-quality images by learning amplitude and phase information separately.

Abstract

The paper proposes a Dual-Domain Feature Fusion Network (DFFN) for low-light remote sensing image enhancement. The key insights are:

Remote sensing images exhibit long-range spatial correlations, which traditional convolutional neural networks struggle to capture. Transformer-based methods can model long-range dependencies but face high computational complexity for high-resolution remote sensing images.

The Fourier transform can decouple the degradation in low-light remote sensing images into amplitude (brightness) and phase (details) information. This motivates a two-stage approach to address the complex coupling problem.

The first stage learns amplitude information to restore image brightness, while the second stage learns phase information to refine details. An Information Fusion Affine Module (IFAM) is designed to facilitate information exchange between the two stages.

Two new datasets, iSAID-dark and darkrs, are introduced to address the lack of paired datasets for low-light remote sensing image enhancement.

Extensive experiments show that the proposed DFFN outperforms state-of-the-art methods on multiple benchmarks, achieving a good balance between model complexity and performance.

Stats

Low-light remote sensing images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space.
Convolutional Neural Networks struggle to establish long-range correlations in such images, while transformer-based methods face high computational complexities.
Fourier transform can compute global information without introducing a large number of parameters, enabling the network to more efficiently capture the overall image structure and establish long-range correlations.

Quotes

"Compared to ordinary images, remote sensing images display richer color information and broader spatial information."
"The continuity of such scenes results in remote sensing images exhibiting long-distance correlations in spatial aggregation."
"It is well-known that Fourier phase preserves high-level semantics, while amplitude contains low-level features."

Key Insights Distilled From

Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement

by Zishu Yao,Gu... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17400.pdf

Deeper Inquiries

How can the proposed DFFN be extended to handle other types of low-quality remote sensing images, such as those affected by atmospheric distortions or sensor degradation?

The proposed DFFN architecture can be extended to handle other types of low-quality remote sensing images by incorporating additional modules or modifications to address specific types of degradation. For atmospheric distortions, which can cause blurring or haze in images, a dehazing module can be integrated into the network. This module could utilize techniques such as dark channel prior or atmospheric scattering models to remove haze and improve image clarity. Additionally, sensor degradation, such as noise or artifacts introduced during image capture, can be mitigated by including denoising or artifact removal modules in the network. These modules can use techniques like wavelet transforms or deep learning-based methods to suppress noise and enhance image quality.
By incorporating these additional modules tailored to specific types of degradation, the DFFN can adapt to a wider range of low-quality remote sensing images and effectively enhance them. The network can be trained on diverse datasets that include various types of degradation to ensure robust performance across different scenarios.

What are the potential limitations of the Fourier domain approach, and how could it be further improved to handle more complex degradation patterns?

While the Fourier domain approach offers advantages in capturing global information and decoupling degradation factors, it also has limitations that can impact its effectiveness in handling more complex degradation patterns. One limitation is the assumption of spatial stationarity, which may not hold true for all types of degradation present in remote sensing images. Complex degradation patterns, such as non-uniform blur or spatially varying noise, may not be effectively addressed by Fourier domain processing alone.
To improve the Fourier domain approach for handling more complex degradation patterns, several enhancements can be considered. One approach is to incorporate adaptive or localized Fourier transforms that can adapt to spatially varying degradation. By segmenting the image into regions with different degradation characteristics and applying Fourier transforms locally, the network can better capture and address complex degradation patterns.
Additionally, combining the Fourier domain approach with spatial domain processing techniques, such as convolutional operations or attention mechanisms, can enhance the network's ability to handle diverse degradation patterns. Hybrid models that integrate the strengths of both domains can leverage the global information provided by Fourier transforms and the spatial context captured by spatial processing, leading to more robust performance in handling complex degradation patterns.

Given the success of the DFFN in low-light remote sensing image enhancement, how could the insights from this work be applied to other image enhancement tasks in the remote sensing domain, such as super-resolution or dehazing?

The insights and architecture of the DFFN can be applied to other image enhancement tasks in the remote sensing domain, such as super-resolution or dehazing, by adapting the network design and modules to suit the specific requirements of each task.
For super-resolution tasks, the DFFN's dual-domain feature fusion approach can be leveraged to combine spatial and frequency domain information to enhance image details and resolution. By incorporating upscaling modules and refining stages similar to the phase refinement phase in DFFN, the network can effectively upscale low-resolution remote sensing images while preserving important details.
In the case of dehazing, the DFFN's ability to separate degradation factors into amplitude and phase components can be utilized to model and remove haze from remote sensing images. By integrating dehazing modules that focus on atmospheric scattering models or dark channel priors, the network can effectively enhance visibility and clarity in hazy images.
Overall, the modular design and information fusion techniques of the DFFN can be adapted and extended to various image enhancement tasks in remote sensing, providing a versatile framework for addressing different types of degradation and improving the overall quality of remote sensing imagery.