DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency

• URL: http://arxiv.org/abs/2501.10110v2
• Date: Mon, 20 Jan 2025 04:00:53 GMT
• Title: DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency
• Authors: Xiaohui Li, Yihao Liu, Shuo Cao, Ziyan Chen, Shaobin Zhuang, Xiangyu Chen, Yinan He, Yi Wang, Yu Qiao,
• Abstract summary: We present DiffVSR, a diffusion-based framework for real-world video super-resolution. For intra-sequence coherence, we develop a multi-scale temporal attention module and temporal-enhanced VAE decoder. We propose a progressive learning strategy that transitions from simple to complex degradations, enabling robust optimization.
• Score: 25.756755602342942
• License:
• Abstract: Diffusion models have demonstrated exceptional capabilities in image generation and restoration, yet their application to video super-resolution faces significant challenges in maintaining both high fidelity and temporal consistency. We present DiffVSR, a diffusion-based framework for real-world video super-resolution that effectively addresses these challenges through key innovations. For intra-sequence coherence, we develop a multi-scale temporal attention module and temporal-enhanced VAE decoder that capture fine-grained motion details. To ensure inter-sequence stability, we introduce a noise rescheduling mechanism with an interweaved latent transition approach, which enhances temporal consistency without additional training overhead. We propose a progressive learning strategy that transitions from simple to complex degradations, enabling robust optimization despite limited high-quality video data. Extensive experiments demonstrate that DiffVSR delivers superior results in both visual quality and temporal consistency, setting a new performance standard in real-world video super-resolution.

Related papers

• Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion [22.988212617368095]
  We propose GLC-Diffusion, a tuning-free method for long video generation. It models the long video denoising process by establishing Global-Local Collaborative Denoising. We also propose a Video Motion Consistency Refinement (VMCR) module that computes the gradient of pixel-wise and frequency-wise losses.
  arXiv  Detail & Related papers  (2025-01-08T05:49:39Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Illumination and Reflection Decomposition [68.6707284662443]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic and static scenes plagued by severe invisibility and noise. One critical aspect is formulating a consistency constraint specifically for temporal-spatial illumination and appearance enhanced versions. We present an innovative video Retinex-based decomposition strategy that operates without the need for explicit supervision.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution [151.1255837803585]
  We propose a novel approach, pursuing Spatial Adaptation and Temporal Coherence (SATeCo) for video super-resolution. SATeCo pivots on learning spatial-temporal guidance from low-resolution videos to calibrate both latent-space high-resolution video denoising and pixel-space video reconstruction. Experiments conducted on the REDS4 and Vid4 datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-03-25T17:59:26Z)
• Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution [65.91317390645163]
  Upscale-A-Video is a text-guided latent diffusion framework for video upscaling. It ensures temporal coherence through two key mechanisms: locally, it integrates temporal layers into U-Net and VAE-Decoder, maintaining consistency within short sequences. It also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation.
  arXiv  Detail & Related papers  (2023-12-11T18:54:52Z)
• ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations [13.38405890753946]
  We introduce a self-supervised method, Consistent Video Restoration through Turbulence (ConVRT) ConVRT is a test-time optimization method featuring a neural video representation designed to enhance temporal consistency in restoration. A key innovation of ConVRT is the integration of a pretrained vision-language model (CLIP) for semantic-oriented supervision.
  arXiv  Detail & Related papers  (2023-12-07T20:19:48Z)
• Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution [15.197746480157651]
  We propose an effective real-world VSR algorithm by leveraging the strength of pre-trained latent diffusion models. We exploit the temporal dynamics in LR videos to guide the diffusion process by optimizing the latent sampling path with a motion-guided loss. The proposed motion-guided latent diffusion based VSR algorithm achieves significantly better perceptual quality than state-of-the-arts on real-world VSR benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-01T14:40:07Z)
• STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction [78.129039340528]
  We propose a StemporalResidual Predictive Model (STRPM) for high-resolution video prediction. STRPM can generate more satisfactory results compared with various existing methods. Experimental results show that STRPM can generate more satisfactory results compared with various existing methods.
  arXiv  Detail & Related papers  (2022-03-30T06:24:00Z)
• Investigating Tradeoffs in Real-World Video Super-Resolution [90.81396836308085]
  Real-world video super-resolution (VSR) models are often trained with diverse degradations to improve generalizability. To alleviate the first tradeoff, we propose a degradation scheme that reduces up to 40% of training time without sacrificing performance. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences.
  arXiv  Detail & Related papers  (2021-11-24T18:58:21Z)
• An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement [132.60976158877608]
  We propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate-temporal-information across frames and reduces the need for high complexity networks.
  arXiv  Detail & Related papers  (2020-12-24T00:03:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.