RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution

• URL: http://arxiv.org/abs/2311.09178v4
• Date: Sun, 10 Dec 2023 23:30:20 GMT
• Title: RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution
• Authors: Marwah Sulaiman, Zahraa Shehabeldin, Israa Fahmy, Mohammed Barakat, Mohammed El-Naggar, Dareen Hussein, Moustafa Youssef, Hesham M. Eraqi
• Abstract summary: We propose Recurrent Back-Projection Generative Adversarial Network (RBPGAN) for video super resolution (VSR) RBPGAN integrates two state-of-the-art models to get the best in both worlds without compromising the accuracy of produced video.
• Score: 2.265171676600799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, video super resolution (VSR) has become a very impactful task in the area of Computer Vision due to its various applications. In this paper, we propose Recurrent Back-Projection Generative Adversarial Network (RBPGAN) for VSR in an attempt to generate temporally coherent solutions while preserving spatial details. RBPGAN integrates two state-of-the-art models to get the best in both worlds without compromising the accuracy of produced video. The generator of the model is inspired by RBPN system, while the discriminator is inspired by TecoGAN. We also utilize Ping-Pong loss to increase temporal consistency over time. Our contribution together results in a model that outperforms earlier work in terms of temporally consistent details, as we will demonstrate qualitatively and quantitatively using different datasets.

Related papers

• SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution [55.14432034345353]
  We study key design principles for latter cascaded video super-resolution models, which are underexplored currently.<n>First, we propose two strategies to generate training pairs that better mimic the output characteristics of the base model, ensuring alignment between the VSR model and its upstream generator.<n>Second, we provide critical insights into VSR model behavior through systematic analysis of (1) timestep sampling strategies, (2) noise augmentation effects on low-resolution (LR) inputs.
  arXiv  Detail & Related papers  (2025-06-24T17:57:26Z)
• Super-Resolution Generative Adversarial Networks based Video Enhancement [0.40964539027092906]
  This work introduces an enhanced approach to video super-resolution by extending ordinary Single-Image-SIS (SRGAN) structure to handle-versarial data.<n>A modified framework that incorporates 3D Non-Local Blocks is developed, which is enabling the model to capture relationships across both spatial and temporal dimensions.<n>Results show improved temporal coherence, sharper textures, and fewer visual artifacts compared to traditional single-image methods.
  arXiv  Detail & Related papers  (2025-05-14T20:16:51Z)
• DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior [13.324336907242195]
  Video-resolution (VSR) aims to reconstruct a high-resolution (HR) video from a low-resolution (LR) counterpart. DC-VSR produces spatially and temporally consistent VSR results with realistic textures. experiments demonstrate that DC-VSR achieves spatially and temporally consistent high-quality VSR results, outperforming previous approaches.
  arXiv  Detail & Related papers  (2025-02-05T10:15:00Z)
• Collaborative Feedback Discriminative Propagation for Video Super-Resolution [66.61201445650323]
  Key success of video super-resolution (VSR) methods stems mainly from exploring spatial and temporal information. Inaccurate alignment usually leads to aligned features with significant artifacts. propagation modules only propagate the same timestep features forward or backward.
  arXiv  Detail & Related papers  (2024-04-06T22:08:20Z)
• 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)
• Gated Recurrent Neural Networks with Weighted Time-Delay Feedback [55.596897987498174]
  We present a novel approach to modeling long-term dependencies in sequential data by introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism.<n>Our proposed model, named $tau$-GRU, is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs)
  arXiv  Detail & Related papers  (2022-12-01T02:26:34Z)
• STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction [78.129039340528]
  We propose a Stemporal Information-Preserving and Perception-Augmented Model (STIP) to solve the above two problems. The proposed model aims to preserve thetemporal information for videos during the feature extraction and the state transitions. Experimental results show that the proposed STIP can predict videos with more satisfactory visual quality compared with a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-09T09:49:04Z)
• 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)
• A Novel Dual Dense Connection Network for Video Super-resolution [0.0]
  Video super-resolution (VSR) refers to the reconstruction of high-resolution (HR) video from the corresponding low-resolution (LR) video. We propose a novel dual dense connection network that can generate high-quality super-resolution (SR) results.
  arXiv  Detail & Related papers  (2022-03-05T12:21:29Z)
• Revisiting Temporal Modeling for Video Super-resolution [47.90584361677039]
  We study and compare three temporal modeling methods (2D CNN with early fusion, 3D CNN with slow fusion and Recurrent Neural Network) for video super-resolution. We also propose a novel Recurrent Residual Network (RRN) for efficient video super-resolution, where residual learning is utilized to stabilize the training of RNN.
  arXiv  Detail & Related papers  (2020-08-13T09:09:37Z)
• MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution [63.02785017714131]
  Video super-resolution (VSR) aims to utilize multiple low-resolution frames to generate a high-resolution prediction for each frame. Inter- and intra-frames are the key sources for exploiting temporal and spatial information. We build an effective multi-correspondence aggregation network (MuCAN) for VSR.
  arXiv  Detail & Related papers  (2020-07-23T05:41:27Z)
• iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks [0.0]
  We present iSeeBetter, a novel GAN-based structural-temporal approach to video super-resolution (VSR) iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Our results demonstrate that iSeeBetter offers superior VSR fidelity and surpasses state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-13T01:36:30Z)
• Video Face Super-Resolution with Motion-Adaptive Feedback Cell [90.73821618795512]
  Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN) In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way.
  arXiv  Detail & Related papers  (2020-02-15T13:14:10Z)

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.