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 t...
This paper proposes AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement, which achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency.
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.
This work proposes ANCHOR, a model-agnostic framework that revisits the low-quality current frame as a temporally aligned anchor for video restoration correction, and estimates a spatial trust field from heterogeneous physical-trace evidence and adaptively balances the restoration proposal with the original observation...
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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