In a typical image inpainting task, the location and shape of the damaged or masked area is often random and irregular. The vanilla convolutions widely used in learning-based inpainting models treat all spatial features as valid and share parameters across regions, making it difficult for them to cope with those irregular damages, and models tend to produce inpainting results with color discrepancy and blurriness. In this paper, we propose a novel Context Adaptive Network (CANet) to address this issue. The main idea of the proposed CANet is able to generate different weights depending on the miscellaneous input, which may help to complement images with multiple broken forms in a flexible way. Specifically, the proposed CANet has two novel context adaptive modules, namely, the context adaptive block (CAB) and the cross-scale contextual attention (CSCA), which utilize attention mechanisms to cope with diverse content breakdowns. The proposed CAB, during the forward propagation, uses an adaptive term to determine the importance between adaptive term and convolution kernel, so as to dynamically balance features based on the degree of breakage (confidence level or soft mask), and the overall calculation is formulated as a classic convolution implementation with an additional attention term to describe local structure. Besides, the proposed CSCA, not only takes advantage of the contextual attention module, but also considers cross-scale information transfer to generate reasonable features for damaged areas, thus alleviating the inefficiency of the long-range modeling capability of convolutional neural networks. Qualitative and quantitative experiments show that our method performs better than state-of-the-arts, producing clearer, more coherent and visually plausible inpainting results. The code can be found at github.com/dengyecode/CANet_image_inpainting.

Cloud cover can significantly hinder the use of remote sensing images for Earth observation, prompting urgent advancements in cloud removal technology. Recently, deep learning strategies, especially convolutional neural networks (CNNs) with attention mechanisms, have shown strong potential in restoring cloud-obscured areas. These methods utilize convolution to extract intricate local features and attention mechanisms to gather long-range information, improving the overall comprehension of the scene. However, a common drawback of these approaches is that the resulting images often suffer from blurriness, artifacts, and inconsistencies. This is partly because attention mechanisms apply weights to all features based on generalized similarity scores, which can inadvertently introduce noise and irrelevant details from cloud-covered areas. To overcome this limitation and better capture relevant distant context, we introduce a novel approach named Attentive Contextual Attention (AC-Attention). This method enhances conventional attention mechanisms by dynamically learning data-driven attentive selection scores, enabling it to filter out noise and irrelevant features effectively. By integrating the AC-Attention module into the DSen2-CR cloud removal framework, we significantly improve the model’s ability to capture essential distant information, leading to more effective cloud removal. Our extensive evaluation of various datasets shows that our method outperforms existing ones regarding image reconstruction quality. Additionally, we conducted ablation studies by integrating AC-Attention into multiple existing methods and widely used network architectures. These studies demonstrate the effectiveness and adaptability of AC-Attention and reveal its ability to focus on relevant features, thereby improving the overall performance of the networks.

Cloud removal aims to restore high-quality images from cloud-contaminated captures, which is essential in remote sensing applications. Effectively modeling the long-range relationships between image features is key to achieving high-quality cloud-free images. While self-attention mechanisms excel at modeling long-distance relationships, their computational complexity scales quadratically with image resolution, limiting their applicability to high-resolution remote sensing images. Current cloud removal methods have mitigated this issue by restricting the global receptive field to smaller regions or adopting channel attention to model long-range relationships. However, these methods either compromise pixel-level long-range dependencies or lose spatial information, potentially leading to structural inconsistencies in restored images. In this work, we propose the Focused-Taylor Attention (FT-Attention), which captures pixel-level long-range relationships without limiting the spatial extent of attention and achieves the O ( N ) computational complexity, where N represents the image resolution. Specifically, we utilize Taylor series expansions to reduce the computational complexity of the attention mechanism from O ( N ² ) to O ( N ), enabling efficient capture of pixel relationships directly in high-resolution images. Additionally, to fully leverage the informative pixel, we develop a new normalization function for the query and key, which produces more distinguishable attention weights, enhancing focus on important features. Building on FT-Attention, we design a U-net style network, termed the CR-former, specifically for cloud removal. Extensive experimental results on representative cloud removal datasets demonstrate the superior performance of our CR-former. The code is available at https://github.com/wuyang2691/CR-former.

Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to reconstruct information under cloud occlusions. While Transformer-based models excel at handling such spatial modeling, their quadratic computational complexity limits practical application. The recently proposed Mamba, a state space model, offers a computationally efficient alternative for long-range modeling, but its inherent 1D sequential processing is ill-suited to capturing complex 2D spatial contexts in images. To bridge this gap, we propose the multi-pattern scanning Mamba (MPSM) block. Our MPSM block adapts the Mamba architecture for vision tasks by introducing a set of diverse scanning patterns that traverse features along horizontal, vertical, and diagonal paths. This multi-directional approach ensures that each feature aggregates comprehensive contextual information from the entire spatial domain. Furthermore, we introduce a dynamic path-aware (DPA) mechanism to adaptively recalibrate feature contributions from different scanning paths, enhancing the model’s focus on position-sensitive information. To effectively capture both global structures and local details, our MPSM blocks are embedded within a U-Net architecture enhanced with multi-scale supervision. Extensive experiments on the RICE1, RICE2, and T-CLOUD datasets demonstrate that our method achieves state-of-the-art performance while maintaining favorable computational efficiency.