Video Super-Resolution Using a Grouped Residual in Residual Network

• URL: http://arxiv.org/abs/2310.11276v1
• Date: Tue, 17 Oct 2023 13:55:43 GMT
• Title: Video Super-Resolution Using a Grouped Residual in Residual Network
• Authors: MohammadHossein Ashoori, and Arash Amini
• Abstract summary: Video super-resolution (VSR) can be considered as the generalization of single image super-resolution (SISR) In this paper, we propose a grouped residual in residual network (GRRN) for VSR.
• Score: 12.691703425623055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement. Video super-resolution (VSR) can be considered as the generalization of single image super-resolution (SISR). This generalization should be such that more detail is created in the output using adjacent input frames. In this paper, we propose a grouped residual in residual network (GRRN) for VSR. By adjusting the hyperparameters of the proposed structure, we train three networks with different numbers of parameters and compare their quantitative and qualitative results with the existing methods. Although based on some quantitative criteria, GRRN does not provide better results than the existing methods, in terms of the quality of the output image it has acceptable performance.

Related papers

• RepNet-VSR: Reparameterizable Architecture for High-Fidelity Video Super-Resolution [12.274092278786966]
  We propose a Reizable Architecture for High Fidelity Video Super Resolution method, named RepNet-VSR, for real-time 4x video super-resolution. The proposed model achieves 27.79 dB PSNR when processing 180p to 720p frames in 103 ms per 10 frames on a MediaTek Dimensity NPU.
  arXiv  Detail & Related papers  (2025-04-22T07:15:07Z)
• Detail-Enhancing Framework for Reference-Based Image Super-Resolution [8.899312174844725]
  We propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution. Our proposed method achieves superior visual results while maintaining comparable numerical outcomes.
  arXiv  Detail & Related papers  (2024-05-01T10:27:22Z)
• Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures [17.517010701323823]
  Super-Resolution (SR) is a time-hallowed image processing problem. We introduce a fully-conversaal Generative Adrial Network (GAN)-based architecture for SR. We show that distinct convolutional features obtained at increasing depths of a GAN generator can be optimally combined by a set of learnable convex weights.
  arXiv  Detail & Related papers  (2024-04-09T13:19:43Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Image Super-resolution with An Enhanced Group Convolutional Neural Network [102.2483249598621]
  CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. We present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture. Experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR.
  arXiv  Detail & Related papers  (2022-05-29T00:34:25Z)
• Deep Networks for Image and Video Super-Resolution [30.75380029218373]
  Single image super-resolution (SISR) is built using efficient convolutional units we refer to as mixed-dense connection blocks (MDCB) We train two versions of our network to enhance complementary image qualities using different loss configurations. We further employ our network for super-resolution task, where our network learns to aggregate information from multiple frames and maintain-temporal consistency.
  arXiv  Detail & Related papers  (2022-01-28T09:15:21Z)
• MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution [74.24676600271253]
  We propose the MASA network for RefSR, where two novel modules are designed to address these problems. The proposed Match & Extraction Module significantly reduces the computational cost by a coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module learns the difference of distribution between the LR and Ref images, and remaps the distribution of Ref features to that of LR features in a spatially adaptive way.
  arXiv  Detail & Related papers  (2021-06-04T07:15:32Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• 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)
• Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction [14.217260022873083]
  As the deep networks go deeper, they become more difficult to train and more difficult to restore the finer texture details. We propose a Residual Channel Attention-Generative Adversarial Network (RCA-GAN) to solve these problems. RCA-GAN yields consistently better visual quality with more detailed and natural textures than baseline models.
  arXiv  Detail & Related papers  (2020-04-28T17:23:46Z)
• 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.