DetDiffRS: A Detail-Enhanced Diffusion Model for Remote Sensing Image Super-Resolution

SONG Miao; CHEN Zhiqiang; WANG Peisong; XING Xiangwei; HUANG Liwei; CHENG Jian

doi:10.11999/JEIT250995

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SONG Miao, CHEN Zhiqiang, WANG Peisong, XING Xiangwei, HUANG Liwei, CHENG Jian. DetDiffRS: A Detail-Enhanced Diffusion Model for Remote Sensing Image Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250995

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SONG Miao, CHEN Zhiqiang, WANG Peisong, XING Xiangwei, HUANG Liwei, CHENG Jian. DetDiffRS: A Detail-Enhanced Diffusion Model for Remote Sensing Image Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250995

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DetDiffRS: A Detail-Enhanced Diffusion Model for Remote Sensing Image Super-Resolution

doi: 10.11999/JEIT250995 cstr: 32379.14.JEIT250995

SONG Miao^{1, 2},
CHEN Zhiqiang³,
WANG Peisong^{1, 2
,
,},
XING Xiangwei⁴,
HUANG Liwei⁴,
CHENG Jian^{1, 2}

1.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Southeast University, Nanjing 210096, China
4.
Beijing Institute of Remote Sensing Information, Beijing 100192, China

Funds: The National Natural Science Foundation of China (62572471, 62341130)

Received Date: 2025-09-25
Accepted Date: 2025-12-24
Rev Recd Date: 2025-11-22

Available Online: 2025-12-31

Abstract

Abstract

Objective This study aims to enhance the reconstruction of fine structural details in high-resolution (HR) remote sensing image super-resolution by leveraging diffusion models. Although diffusion-based approaches have achieved remarkable success in natural image restoration, their direct application to remote sensing imagery remains suboptimal due to the pronounced imbalance between extensive low-frequency homogeneous regions (e.g., water bodies, farmland) and localized high-frequency regions with complex structures (e.g., buildings, ports, aircraft). This imbalance often leads to insufficient learning of crucial high-frequency details, resulting in reconstructions that appear globally smooth but lack sharpness and realism. To overcome this limitation, we propose DetDiffRS—a detail-enhanced diffusion-based framework—designed to explicitly improve the model’s sensitivity to high-frequency information during both data sampling and optimization, thereby achieving superior perceptual quality and structural fidelity. Methods The proposed DetDiffRS framework introduces innovations at both the data input and loss function levels to mitigate the high–low frequency imbalance in remote sensing imagery. First, a Multi-Scale Patch Sampling (MSPS) strategy is developed to increase the likelihood of selecting patches containing high-frequency structures during training. This is achieved by constructing a multi-scale patch pool and applying weighted sampling to prioritize complex regions. Second, a composite perceptual loss is designed to provide richer supervision beyond conventional denoising objectives. This loss integrates: (1) a High-Dimensional Perceptual Loss (HDPL) to enforce structural consistency in deep feature space, and (2) a High-Frequency-Aware Loss (HFAL) to directly constrain high-frequency components in the frequency domain. The combination of MSPS and the composite perceptual loss enables the diffusion model to more effectively capture and reconstruct fine details, thereby improving both objective quality metrics and visual realism. Results and Discussions Extensive experiments were conducted on three publicly available remote sensing datasets—AID, DOTA, and DIOR—against a diverse set of state-of-the-art super-resolution methods, including CNN-based (EDSR, RCAN), Transformer-based (HAT-L, TTST), GAN-based (MSRGAN, ESRGAN, SPSR), and diffusion-based (SR3, IRSDE) approaches. Quantitative evaluation using Fréchet Inception Distance (FID) on the AID dataset demonstrated that DetDiffRS achieved the best performance in 21 out of 30 scene categories, yielding an average FID of 48.37, surpassing the second-best method by 1.14. The improvements were particularly pronounced in texture-rich and structurally complex categories such as Dense Residential, Meadow, and River, where FID reductions exceeded 3.0 compared to competing diffusion models (Table 1). While PSNR-oriented methods like RCAN obtained the highest PSNR/SSIM values in some cases, they produced overly smooth reconstructions lacking fine details. In contrast, DetDiffRS, benefiting from its High-Dimensional Perceptual Loss (HDPL) and High-Frequency-Aware Loss (HFAL), achieved a balanced enhancement in both objective metrics and perceptual quality—for instance, improving PSNR by 1.06 dB over SR3 on AID and SSIM by 0.0846 on DOTA (Table 2). Visual comparisons further confirmed that DetDiffRS consistently generated sharper edges, clearer structures, and more realistic textures, effectively mitigating the over-smoothing of PSNR-focused methods and the artifacts often observed in GAN-based approaches (Fig. 5, Fig. 6). Conclusions This study presents DetDiffRS, a detail-enhanced diffusion-based super-resolution framework tailored for the unique frequency distribution characteristics of remote sensing imagery. By integrating the Multi-Scale Patch Sampling (MSPS) strategy with a composite perceptual loss combining HDPL and HFAL, the proposed method effectively addresses the underrepresentation of high-frequency regions during training, leading to substantial improvements in detail preservation and perceptual fidelity. Experimental results across multiple datasets and diverse scene types demonstrate that DetDiffRS not only outperforms existing CNN-, Transformer-, GAN-, and diffusion-based methods in FID, but also achieves a competitive balance between PSNR/SSIM and visual realism. These findings suggest that DetDiffRS offers a robust and generalizable solution for high-quality remote sensing image super-resolution, with strong potential for application in urban planning, environmental monitoring, and other geospatial analysis tasks requiring both structural accuracy and fine detail reconstruction.
- Remote sensing super-resolution(RSSR),
- Diffusion model(DM),
- High-frequency detail restoration

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