TL;DR
This work proposes FaverNet, a Frequency-guided All-in-one VidEo Restoration Network, which incorporates a Frequency-discriminative Conditioning Mechanism (FCM) and a Prompt-guided Alignment Mechanism (PAM), which enhances the degradation-awareness of the restoration process by conditioning the model with degradation cues extracted from frequency domain.
The Large Processing Model is presented, a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degradations, demonstrating that generative restoration can be practical, scalable, and cost-effective for large-scale video processing.
High-Compression videos suffer from severe distortions, among which degradation in person regions has the greatest impact on viewers’ immersive experience. Existing quality enhancement techniques usually focus on overall image denoising or super-resolution, often overlooking the crucial recovery of fine structures in these essential person regions. To address these challenges, the research introduces a novel framework titled Person Region Restoration Driven by Perceptual Fidelity (PRRDPF), which combines long-range dependency features with perceptual structure loss for enhanced generative restoration. Specifically, first, the research constructs a high-fidelity distorted person-region dataset via a closed-loop degradation pipeline, addressing the lack of paired datasets. Secondly, a Temporal Gated Fusion (TGF) block is designed to use gated convolutions for selectively recovering high-frequency features while capturing local and global dependencies. Finally, a Structural Similarity Index Measure (SSIM)-based dynamic weighted adversarial loss is proposed to prioritize the restoration of visual texture details. Experimental results validate that PRRDPF significantly outperforms the best models in Peak Signal-to-Noise Ratio (PSNR), SSIM, and Learned Perceptual Image Patch Similarity (LPIPS), effectively mitigating artifacts and enhancing clarity in person visuals. This framework presents a promising approach for intelligent video coding integrated with generative artificial intelligence and holds significant potential for practical applications.
All-in-one image restoration has recently attracted considerable attention for its ability to address multiple degradation types within a single, unified framework. However, existing methods often incur substantial computational overhead, especially when incorporating explicit degradation priors via complex auxiliary branches, hindering their practical deployment. In this paper, we propose AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement. Recognizing that different degradations impact distinct frequency subbands and exhibit spatially varying restoration demands, we design an Adaptive Frequency Enhancement Module (AFEM) that couples frequency learning with adaptive convolutions to better capture frequency-aware information. Specifically, AFEM learns pixel-wise adaptive attention weights to modulate the spectra of dynamic convolutions, enabling spatially adaptive and content-aware restoration. Furthermore, we introduce a lightweight backbone featuring a Receptive Field Expansion Module (RFEM), which enlarges the receptive field of a convolutional U-shaped architecture by convolving wavelet-transform coefficients. By integrating the plug-and-play AFEM into the bottleneck of the baseline model, AdaptIR achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency. Moreover, the proposed model can be readily extended to single-degradation tasks (e.g., dehazing, desnowing, and deraining) and domain-specific applications, including ultra-high-definition (UHD), medical, and remote sensing image restoration.
DPCA-Net establishes a superior Pareto front between restoration fidelity and efficiency by maintaining real-time inference speeds with a minimal footprint of 15.00 GFLOPs, offering a compelling practical solution for real-time all-in-one image restoration.
Video restoration methods exploit temporal information to recover information missing from degraded observations. However, reference frames within the sequence may introduce inconsistent degradation, content discrepancy, or reconstruction errors due to physical image-formation variations, occlusion, and imperfect temporal aggregation. Existing approaches mainly focus on improving restoration networks, while the reliability of the generated outputs at different spatial locations remains largely unexplored. In this work, we propose ANCHOR, a model-agnostic framework that revisits the low-quality current frame as a temporally aligned anchor for video restoration correction. Specifically, ANCHOR estimates a spatial trust field from heterogeneous physical-trace evidence and adaptively balances the restoration proposal with the original observation. Experiments on High Dynamic Range video reconstruction and video deraining demonstrate consistent improvements across various state-of-the-art restoration models, validating the effectiveness of reliability-aware output correction for video restoration.
OminiVR surpasses all prior methods on all six visual metrics, achieves the best audio quality, and produces natural colorization---the first method to jointly address all three aspects of audio-video generative restoration.
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