Multi-Depth Branch Network for Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2309.17334v2
• Date: Mon, 15 Jan 2024 09:05:34 GMT
• Title: Multi-Depth Branch Network for Efficient Image Super-Resolution
• Authors: Huiyuan Tian, Li Zhang, Shijian Li, Min Yao, Gang Pan
• Abstract summary: A longstanding challenge in Super-Resolution (SR) is how to efficiently enhance high-frequency details in Low-Resolution (LR) images. We propose an asymmetric SR architecture featuring Multi-Depth Branch Module (MDBM) MDBMs contain branches of different depths, designed to capture high- and low-frequency information simultaneously and efficiently.
• Score: 12.042706918188566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A longstanding challenge in Super-Resolution (SR) is how to efficiently enhance high-frequency details in Low-Resolution (LR) images while maintaining semantic coherence. This is particularly crucial in practical applications where SR models are often deployed on low-power devices. To address this issue, we propose an innovative asymmetric SR architecture featuring Multi-Depth Branch Module (MDBM). These MDBMs contain branches of different depths, designed to capture high- and low-frequency information simultaneously and efficiently. The hierarchical structure of MDBM allows the deeper branch to gradually accumulate fine-grained local details under the contextual guidance of the shallower branch. We visualize this process using feature maps, and further demonstrate the rationality and effectiveness of this design using proposed novel Fourier spectral analysis methods. Moreover, our model exhibits more significant spectral differentiation between branches than existing branch networks. This suggests that MDBM reduces feature redundancy and offers a more effective method for integrating high- and low-frequency information. Extensive qualitative and quantitative evaluations on various datasets show that our model can generate structurally consistent and visually realistic HR images. It achieves state-of-the-art (SOTA) results at a very fast inference speed. Our code is available at https://github.com/thy960112/MDBN.

Related papers

• DSR-Diff: Depth Map Super-Resolution with Diffusion Model [38.68563026759223]
  We present a novel CDSR paradigm that utilizes a diffusion model within the latent space to generate guidance for depth map super-resolution. Our proposed method has shown superior performance in extensive experiments when compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-16T14:18:10Z)
• RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution [57.98314517861539]
  Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network.
  arXiv  Detail & Related papers  (2023-06-30T12:14:13Z)
• Learning Detail-Structure Alternative Optimization for Blind Super-Resolution [69.11604249813304]
  We propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR. In our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. Our method achieves the state-of-the-art against existing methods.
  arXiv  Detail & Related papers  (2022-12-03T14:44:17Z)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Deep Burst Super-Resolution [165.90445859851448]
  We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset.
  arXiv  Detail & Related papers  (2021-01-26T18:57:21Z)
• Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images [44.85260985973405]
  Cross-MPI is an end-to-end RefSR network composed of a novel plane-aware MPI mechanism, a multiscale guided upsampling module and a super-resolution synthesis and fusion module. Experimental results on both digitally synthesized and optical zoom cross-scale data show that the Cross-MPI framework can achieve superior performance against the existing RefSR methods.
  arXiv  Detail & Related papers  (2020-11-30T09:14:07Z)
• Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network [63.69237156340457]
  We present and advocate an explainable approach toward SISR named model-guided deep unfolding network (MoG-DUN) MoG-DUN is accurate (producing fewer aliasing artifacts), computationally efficient (with reduced model parameters), and versatile (capable of handling multiple degradations) The superiority of the proposed MoG-DUN method to existing state-of-theart image methods including RCAN, SRDNF, and SRFBN is substantiated by extensive experiments on several popular datasets and various degradation scenarios.
  arXiv  Detail & Related papers  (2020-09-14T08:23:37Z)
• Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks [32.10057746890683]
  We propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image and a high-resolution multispectral image. The proposed network architecture achieves best performance compared with recent state-of-the-art hyperspectral image super-resolution approaches.
  arXiv  Detail & Related papers  (2020-05-29T05:56:50Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.