Recently, remote sensing super-resolution (SR) tasks have been widely studied and achieved remarkable performance. However, due to the complex texture and serious image degeneration, the conventional methods (e.g. CNN-based, GAN-based) cannot reconstruct high-resolution (HR) remote sensing images with a large SR factor (≥ ×8). In this paper, we model the large-factor super-resolution (LFSR) task as a referenced diffusion process and explore how to embed pixel-wise constraint into the popular diffusion model. Following this motivation, we propose the first diffusion-based LFSR method named texture consistency diffusion model (TCDM) for remote sensing images. Specifically, we build a novel conditional truncated noise generator (CTNG) in TCDM to simultaneously generate the expectation of posterior probability p ( x _t-1 | x _t ) and the truncated noise image. With the predicted truncated noise image, sampling an SR image using CTNG saves nearly 90% processing time compared to the naive diffusion model. Additionally, we design a new denoising process named texture consistency diffusion (TC-diffusion) to explicitly embed pixel-wise constraints into the LFSR diffusion model during the training stage. Universal experiments on five commonly used remote sensing datasets demonstrate that the proposed TCDM surpasses the latest SR methods by a large margin and reports new SOTA results on several evaluation metrics. Additionally, the proposed method demonstrates impressive visual quality on reconstructed remote sensing image texture and details.

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks.