Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications.

With the wide application of remote sensing images (RSIs) in military and civil fields, remote sensing object detection (RSOD) has gradually become a hot research direction. However, we observe two main challenges for remote sensing object detection, namely the complicated background and the small objects issues. Given the different appearances of generic objects and remote sensing objects, the detection algorithms designed for the former usually cannot perform well for the latter. We propose a novel global to local scale-aware detection network (GLSANet) for remote sensing object detection, aiming to solve the above mentioned two challenges. Firstly, we design a global semantic information interaction module (GSIIM) to excavate and reinforce the high-level semantic information in the deep feature map, which alleviates the obstacles of complex background on foreground objects. Secondly, we optimize the feature pyramid network to improve the performance of multiscale object detection in RSIs. Finally, a local attention pyramid (LAP) is introduced to highlight the feature representation of small objects gradually while suppressing the background and noise in the shallower feature maps. Extensive experiments on three public datasets demonstrate that the proposed method achieves superior performance compared with the state-of-the-art detectors, especially on small object detection dataset. Specifically, our algorithm reaches 94.57% mAP on NWPU VHR-10 dataset, 95.93% mAP on RSOD dataset and 77.9% mAP on DIOR dataset, respectively.

Adverse weather conditions, such as rain, raindrop, snow and haze, consistently degrade images in an unpredictable manner, thereby rendering existing task-specific and task-aligned methods inadequate in addressing this formidable problem. To this end, we investigate the application of Transformer in image restoration and introduce an efficient frequency-oriented method called AIRFormer, which is designed to restore weather-degraded images comprehensively and holistically. Specifically, we identify that the initial self-attention mechanism exhibits distinctive properties akin to a low-pass filter. Therefore, we construct a frequency-guided Transformer encoder by incorporating wavelet-based prior information to guide the extraction of image features. Additionally, considering the non-specific frequency characteristics of self-attention in the later stages, we develop a frequency-refined Transformer decoder that incorporates learnable task-specific queries across spatial dimensions, channel dimensions, and wavelet domains. To facilitate the training of our proposed method, we curate a comprehensive benchmark dataset named AIR40K that, encompasses a wide range of challenging scenarios. Extensive experimental evaluations demonstrate the superiority of our AIRFormer over both task-aligned and all-in-one methods across 15 publicly available datasets. Notably, AIRFormer achieves the best trade-off between the inference time and quality of reconstructed image, comparing with existing methods such as TransWeather and Restormer. The source code, dataset and pre-trained models will be available at https://github.com/chdwyb/AIRFormer.

Current methods for remote sensing image dehazing confront noteworthy computational intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic applicability. To this end, we propose EMPF-Net, a novel encoder-free multi-axis physics-aware fusion network that exhibits both light-weighted characteristics and computational efficiency. In our pipeline, we contend that conventional u-shaped networks allocate substantial computational resources to encode haze-degraded features, which play a subordinate role in the reconstruction process. Consequently, our encoder stages solely incorporate down-sampling operations. To improve the representation efficiency and enhance the generalization capabilities, we devise a multi-axis partial queried learning block (MPQLB) that primarily concentrates on learning dimension-wise queries, instead of relying solely on strictly-correlated content of the input features. Furthermore, we augment the reconstruction procedure by incorporating ground truth supervision into each stage via a supervised cross-scale transposed attention module (SCTAM). It calculates attention maps under the guidance of clean images, thereby suppressing less informative features to propagate to the subsequent level. In addition, to address the challenge of ineffective intral-level feature fusion, which result in insufficient elimination of haze-degraded information and negatively impact the quality of reconstructed images, we introduce a physics-aware intra-level fusion module (PIFM). This module harnesses a physical inversion model to facilitate the intra-level feature interaction and alleviate the interference of dehazing-irrelevant information. Our proposed EMPF-Net is evaluated on 12 publicly available datasets, and the experimental results substantiate our superiority in terms of both metrical scores and visual quality, despite being equipped with a modest parameter count of 300 K. Our approach is readily accessible at https://github.com/chdwyb/EMPF-Net.

Remote sensing object detection (RSOD) encounters challenges in complex backgrounds and small object detection, which are interconnected and unable to be addressed separately. To this end, we propose an attention-free global multiscale fusion network (AGMF-Net). Initially, we present a spatial bias module (SBM) to obtain long-range dependencies as a part of our proposal global information extraction module (GIEM). GIEM efficiently captures the global information, overcoming challenges posed by complex backgrounds. Moreover, we propose multi-task enhanced structure (MES) and multi-task feature pre-treatment (MFP) to enhance the feature representation of multiscale targets, while eliminating the interference from complex backgrounds. In addition, an efficient context decoupled detector (ECDD) is presented to provide distinct features for regression and classification tasks, aiming to improve the efficiency of RSOD. Extensive experiments demonstrate that our proposed method achieves superior performance compared with the state-of-the-art detectors. Specifically, AGMF-Net obtains the mean average precision (mAP) of 73.2%, 92.03%, 95.21% and 94.30% on DIOR, HRRSD, NWPU VHR-10 and RSOD datasets, respectively.

The removal of rain streaks and raindrops is crucial for enhancing the image visibility and mitigating the weather degradations. However, most existing approaches rely on the paired rainy and clean images, which are challenging to obtain in real-world scenarios. To this end, we propose a novel structure-preserving frequency-regularized text-guided optimal transport (SFTOT) framework, which formulates the unpaired rain streaks and raindrops removal as an optimal transport problem. Specifically, we introduce a structure-preserving transport cost, incorporating the structural similarity constraint to minimize the duality gap between the primal and dual formulations, while preserving the structural details of reconstructed images. Furthermore, by embedding the inherent frequency sparsity of rain streaks and raindrops into the transport cost, we derive a frequency-regularized optimal transport objective, ensuring consistency in frequency distributions between the generated and clean images. Additionally, we employ a pre-trained one-step stable diffusion model as the restoration network, which is fine-tuned using the low-rank adaptation (LoRA) adapters and zero convolutional layers, while integrating the domain-specific text prompts for both degraded and clean images to guide the generation process. Extensive experiments demonstrate that our method surpasses the existing well-performing unpaired learning approaches, achieving notable improvements in both the fidelity and photo-realism.