The rapid progress of generative models has made detecting realistic forgeries a critical challenge for security and trust. Existing image and frequency-based methods depend on dataset-specific artifacts with poor generalization, while Vision-Language Model (VLM)-based methods remain limited by coarse prompts and underused cross-modal alignment. To address these issues, we propose a Fine-grained Text-driven Generative Image Detection (FTGID) framework, which enables comprehensive detection through multi-modal cues. First, we design a Layer-wise Adaptive Global Extractor (LAGE) that stabilizes multi-level global representations through adaptive CLS token fusion with lightweight calibration and parameter-efficient tuning. Second, we propose a Fine-grained Text-guided Local Enhancer (FTLE) that performs patch-level text-visual interaction to enhance the localization of forgery-relevant regions. Third, we introduce a High-frequency Artifact Feature Extractor (HAFE) that adaptively captures discriminative high-frequency cues, enabling more reliable detection of subtle generative artifacts. Extensive experiments demonstrate that FTGID consistently outperforms state-of-the-art GID methods across diverse generative models and unseen datasets, achieving superior performance, thereby enhancing both robustness and interpretability in open-world generative image detection. Our codes will be made publicly available after the peer review process.

Videos are generally compressed to save storage and transmission bandwidth. Popular lossy video compression inevitably leads to Perceivable Encoding Artifacts (PEAs) that affect user's visual experience. Thus, Compression Artifact Removal (CAR) methods have emerged to eliminate perceivable encoding artifacts after video coding. However, there still lacks of an efficient artifact discrimination and evaluation method to guide the optimization of CAR methods. To solve this problem, we make the first attempt to propose an Artifact Perception and Evaluation Network (APE-Net) that can accurately locate artifacts and evaluate their impacts on user experience. First, we propose an Artifact Perception Module (APM) that captures various types and long-tailed-distributed PEAs with attention learning and data re-weighting, thus greatly improving the perception capability for video compression artifacts. Second, we design an Artifact Evaluation Module (AEM) to fuse all recognized PEAs with visual saliency and random forest regression, which assists the artifact perception model to be in line with human visual characteristics in video quality assessment tasks. Experimental results demonstrate that our proposed APE-Net is superior to the state-of-the-art algorithms on compressed video quality assessment. Our codes will be made publicly available after the peer review process.

Video deinterlacing remains a significant challenge due to structural artifacts and information loss. When displayed on modern digital devices, early interlaced videos often suffer from complex interlacing and compression artifacts, which severely degrade visual quality. Existing deinterlacing methods typically struggle to handle such diverse artifacts while preserving fine-grained details. To address these issues, we propose a novel Spatiotemporal Synchronization for Video Deinterlacing (SSVD). First, we design a Multi-Directional Shuffling Module (MDSM) to enhance the model's ability to capture spatial dependencies, thereby guiding the prediction of missing fields. Second, a Dynamic Cross-frame Interaction Module (DCIM) is incorporated to implicitly model inter-frame correspondences, effectively leveraging cross-frame information to alleviate the blurring and artifacts. Third, we develop a Gated Refinement Module (GRM) to achieve fine-grained reconstruction. Experimental results demonstrate SSVD is superior to the state-of-the-art algorithms in video deinterlacing tasks, achieving superior visual quality and detail reconstruction. Source code will be made public after the review is completed.

Video distortion seriously affects user experience and downstream tasks. Existing video restoration methods still suffer from high-frequency detail loss, limited spatio-temporal dependency modeling, and high computational complexity. In this letter, we propose a novel video restoration method based on full-frequency spatio-temporal information enhancement (FFSTIE). The proposed FFSTIE includes an implicit alignment module for accurate recovery of high-frequency details and a full-frequency feature reconstruction module for adaptive enhancement of frequency components. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our FFSTIE method. On the video deblurring dataset DVD, FFSTIE achieves 0.75% improvement in PSNR and 1.08% improvement in SSIM with 35% fewer parameters and 59% lower GMAC compared to VDTR (TCSVT'2023), achieving a balance between performance and efficiency. On the video denoising dataset DAVIS, FFSTIE achieves the best performance with an average of 35.36 PSNR and 0.9347 SSIM, surpassing existing unsupervised methods.

Aerial imaging aims to produce well-exposed images with rich details. However, aerial photography may encounter low-light conditions during dusk or dawn, as well as on cloudy or foggy days. In such low-light scenarios, aerial images often suffer from issues such as underexposure, noise, and color distortion. Most existing low-light imaging methods struggle with achieving realistic exposure and retaining rich details. To address these issues, we propose an Aerial Low-light Imaging with Color-monochrome Engagement (ALICE), which employs a coarse-to-fine strategy to correct low-light aerial degradation. First, we introduce wavelet transform to design a perturbation corrector for coarse exposure recovery while preserving details. Second, inspired by the binocular low-light imaging mechanism of the human visual system, we introduce uniformly well-exposed monochrome images to guide a refinement restorer, processing luminance and chrominance branches separately for further improved reconstruction. Within this framework, we design a Reference-based Illumination Fusion Module (RIFM) and an Illumination Detail Transformation Module (IDTM) for targeted exposure and detail restoration. Third, we develop a Dual-camera Low-light Aerial Imaging (DuLAI) dataset to evaluate our proposed ALICE. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our ALICE, achieving a PSNR improvement of at least 19.52% over 12 state-of-the-art methods on the DuLAI Syn-R1440 dataset, while providing more balanced exposure and richer details. Our codes and datasets will be made publicly available after the peer review process.

Image enhancement methods have been widely studied to improve the visual quality of diverse images, implicitly assuming that all human observers have normal vision. However, a large population around the world suffers from Color Vision Deficiency (CVD). Enhancing images to compensate for their perceptions remains a challenging issue. Existing CVD compensation methods have two drawbacks: first, the available datasets and validations have not been rigorously tested by CVD individuals; second, these methods struggle to strike an optimal balance between contrast enhancement and naturalness preservation, which often results in suboptimal outcomes for individuals with CVD. To address these issues, we develop the first large-scale, CVD-individual-labeled dataset called FZU-CVDSet and a CVD-friendly recoloring algorithm called ColorAssist. In particular, we design a perception-guided feature extraction module and a perception-guided diffusion transformer module that jointly achieve efficient image recoloring for individuals with CVD. Comprehensive experiments on both FZU-CVDSet and subjective tests in hospitals demonstrate that the proposed ColorAssist closely aligns with the visual perceptions of individuals with CVD, achieving superior performance compared with the state-of-the-arts. The source code is available at https://github.com/xsx-fzu/ColorAssist.

Natural video capturing suffers from visual blurriness due to high-motion of cameras or objects. Until now, the video blurriness removal task has been extensively explored for both human vision and machine processing. However, its computational cost is still a critical issue and has not yet been fully addressed. In this paper, we propose a novel Lightweight Video Deblurring (LightViD) method that achieves the top-tier performance with an extremely low parameter size. The proposed LightViD consists of a blur detector and a deblurring network. In particular, the blur detector effectively separate blurriness regions, thus avoid both unnecessary computation and over-enhancement on non-blurriness regions. The deblurring network is designed as a lightweight model. It employs a Spatial Feature Fusion Block (SFFB) to extract hierarchical spatial features, which are further fused by ConvLSTM for effective spatial-temporal feature representation. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our LightViD method, which achieves competitive performances on GoPro and DVD datasets, with reduced computational costs of 1.63M parameters and 96.8 GMACs. Trained model available: https://github.com/wgp/LightVid.

Video compression leads to compression artifacts, among which Perceivable Encoding Artifacts (PEAs) degrade user perception. Most of existing state-of-the-art Video Compression Artifact Removal (VCAR) methods indiscriminately process all artifacts, thus leading to over-enhancement in non-PEA regions. Therefore, accurate detection and location of PEAs is crucial. In this paper, we propose the largest-ever Fine-grained PEA database (FPEA). First, we employ the popular video codecs, VVC and AVS3, as well as their common test settings, to generate four types of spatial PEAs (blurring, blocking, ringing and color bleeding) and two types of temporal PEAs (flickering and floating). Second, we design a labeling platform and recruit sufficient subjects to manually locate all the above types of PEAs. Third, we propose a voting mechanism and feature matching to synthesize all subjective labels to obtain the final PEA labels with fine-grained locations. Besides, we also provide Mean Opinion Score (MOS) values of all compressed video sequences. Experimental results show the effectiveness of FPEA database on both VCAR and compressed Video Quality Assessment (VQA). We envision that FPEA database will benefit the future development of VCAR, VQA and perception-aware video encoders. The FPEA database has been made publicly available.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this letter, we investigate the influence of four spatial PEAs (i.e.blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e.flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

Video quality assessment is critical in optimizing video coding techniques. However, the state-of-the-art methods have limited performance, which is largely due to the lack of large-scale subjective databases for training. In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload. The resulting Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far the largest of compressed video quality database. We train a no-reference compressed video quality assessment model with a 3D CNN for SpatioTemporal Feature Extraction and Evaluation (STFEE). Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The CVSAR database has been made publicly available. It can be accessed athttps://github.com/Rocknroll194/CVSAR.

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Quality-of-Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subject-labeled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. Based on the PEA265 database, we develop and optimize Convolutional Neural Networks (CNNs) to objectively recognize different types of PEAs. Experiments show that our architecture is capable of identifying the 6 types of PEAs with an accuracy over 86%. To further demonstrate its application, we explore the relationship between collected PEA intensities and subjective quality scores of compressed videos. A quality metric is consequently proposed with superior performance in terms of correlation to Mean Opinion Score (MOS) values. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.