Existing free-energy guided No-Reference Image Quality Assessment (NR-IQA) methods continue to face challenges in effectively restoring complexly distorted images. The features guiding the main network for quality assessment lack interpretability, and efficiently leveraging high-level feature information remains a significant challenge. As a novel class of state-of-the-art (SOTA) generative model, the diffusion model exhibits the capability to model intricate relationships, enhancing image restoration effectiveness. Moreover, the intermediate variables in the denoising iteration process exhibit clearer and more interpretable meanings for high-level visual information guidance. In view of these, we pioneer the exploration of the diffusion model into the domain of NR-IQA. We design a novel diffusion model for enhancing images with various types of distortions, resulting in higher quality and more interpretable high-level visual information. Our experiments demonstrate that the diffusion model establishes a clear mapping relationship between image reconstruction and image quality scores, which the network learns to guide quality assessment. Finally, to fully leverage high-level visual information, we design two complementary visual branches to collaboratively perform quality evaluation. Extensive experiments are conducted on seven public NR-IQA datasets, and the results demonstrate that the proposed model outperforms SOTA methods for NR-IQA. The codes will be available at https://github.com/handsomewzy/DiffV2IQA.

Constrained by imaging systems, hyperspectral images (HSIs) always have a low spatial resolution. Deep learning-based HSI super-resolution methods have achieved impressive results through learning the nonlinear mapping between low-resolution (LR) and high-resolution (HR) images. However, most of them take the LR image or its upsampled version through bicubic interpolation as input, leading to low-quality features and limited details captured by the network. As a powerful generative model, diffusion model has the ability to learn both contextual semantics and textual details from distinct timesteps, enabling the effective exploration of spatial-spectral distributions in high-dimensional data. In this paper, we propose a novel method that extracts high-quality prior information from original images to assist in super-resolution through pretraining a diffusion model. Specifically, we first train a diffusion model using original HSI patches in a self-supervised manner and then obtain prior features from the pretrained denoising U-Net decoder. To efficiently incorporate the prior features into the super-resolution model, we propose an adaptive fusion module based on spatial and spectral attention mechanisms, which enhances features in both dimensions while preserving the original characteristics. Additionally, to leverage the complementarity of spatial and spectral information, we design a spatial-spectral aggregation Transformer module that incorporates an adaptive interaction module to facilitate information exchange across different dimensions, thereby enhancing the representation capability. Extensive experiments on three public hyperspectral datasets demonstrate that the proposed method achieves excellent super-resolution performance and outperforms the state-of-the-art methods in terms of quantitative quality and visual results.

Remote-sensing images typically feature large dimensions and contain repeated texture patterns. To effectively capture finer details and encode comprehensive information, feature-extraction networks with larger receptive fields are essential for remote-sensing image super-resolution tasks. However, mainstream methods based on stacked Transformer modules suffer from limited receptive fields due to fixed window sizes, impairing long-range dependency capture and fine-grained texture reconstruction. In this paper, we propose a spatial-frequency joint attention network based on multi-window fusion (MWSFA). Specifically, our approach introduces a multi-window fusion strategy, which merges windows with similar textures to allow self-attention mechanisms to capture long-range dependencies effectively, therefore expanding the receptive field of the feature extractor. Additionally, we incorporate a frequency-domain self-attention branch in parallel with the original Transformer architecture. This branch leverages the global characteristics of the frequency domain to further extend the receptive field, enabling more comprehensive self-attention calculations across different frequency bands and better utilization of consistent frequency information. Extensive experiments on both synthetic and real remote-sensing datasets demonstrate that our method achieves superior visual reconstruction effects and higher evaluation metrics compared to other super-resolution methods.

State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data. Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames. The proposed modular architecture is designed for seamless integration with existing VSR frameworks, ensuring strong adaptability and transferability across diverse applications. Experimental results demonstrate that our method achieves performance on par with, or surpassing, the current SOTA models, while significantly reducing inference time. By addressing key bottlenecks in CVSR, our work offers a practical and efficient pathway for advancing VSR technology. Our code will be publicly available at https://github.com/handsomewzy/FCA2.

Existing nighttime aerial trackers based on prompt learning rely solely on spatial localization supervision, which fails to provide fine-grained cues that point to target features and inevitably produces vague prompts. This limitation impairs the tracker's ability to accurately focus on the object features and results in trackers still performing poorly. To address this issue, we propose DPTrack, a prompt-based aerial tracker designed for nighttime scenarios by encoding the given object's attribute features into the directional kernel enriched with fine-grained cues to generate precise prompts. Specifically, drawing inspiration from visual bionics, DPTrack first hierarchically captures the object's topological structure, leveraging topological attributes to enrich the feature representation. Subsequently, an encoder condenses these topology-aware features into the directional kernel, which serves as the core guidance signal that explicitly encapsulates the object's fine-grained attribute cues. Finally, a kernel-guided prompt module built on channel-category correspondence attributes propagates the kernel across the features of the search region to pinpoint the positions of target features and convert them into precise prompts, integrating spatial gating for robust nighttime tracking. Extensive evaluations on established benchmarks demonstrate DPTrack's superior performance. Our code will be available at https://github.com/zzq-vipsl/DPTrack.