RBSR: Efficient and Flexible Recurrent Network for Burst
Super-Resolution
- URL: http://arxiv.org/abs/2306.17595v2
- Date: Thu, 31 Aug 2023 13:45:28 GMT
- Title: RBSR: Efficient and Flexible Recurrent Network for Burst
Super-Resolution
- Authors: Renlong Wu, Zhilu Zhang, Shuohao Zhang, Hongzhi Zhang and Wangmeng Zuo
- Abstract summary: 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.
- Score: 57.98314517861539
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Burst super-resolution (BurstSR) aims at reconstructing a high-resolution
(HR) image from a sequence of low-resolution (LR) and noisy images, which is
conducive to enhancing the imaging effects of smartphones with limited sensors.
The main challenge of BurstSR is to effectively combine the complementary
information from input frames, while existing methods still struggle with it.
In this paper, we suggest fusing cues frame-by-frame with an efficient and
flexible recurrent network. In particular, we emphasize the role of the
base-frame and utilize it as a key prompt to guide the knowledge acquisition
from other frames in every recurrence. Moreover, we introduce an implicit
weighting loss to improve the model's flexibility in facing input frames with
variable numbers. Extensive experiments on both synthetic and real-world
datasets demonstrate that our method achieves better results than
state-of-the-art ones. Codes and pre-trained models are available at
https://github.com/ZcsrenlongZ/RBSR.
Related papers
- Low-Res Leads the Way: Improving Generalization for Super-Resolution by
Self-Supervised Learning [45.13580581290495]
This work introduces a novel "Low-Res Leads the Way" (LWay) training framework to enhance the adaptability of SR models to real-world images.
Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images, merging them with super-resolved outputs for LR reconstruction.
Our training regime is universally compatible, requiring no network architecture modifications, making it a practical solution for real-world SR applications.
arXiv Detail & Related papers (2024-03-05T02:29:18Z) - Efficient Model Agnostic Approach for Implicit Neural Representation
Based Arbitrary-Scale Image Super-Resolution [5.704360536038803]
Single image super-resolution (SISR) has experienced significant advancements, primarily driven by deep convolutional networks.
Traditional networks are limited to upscaling images to a fixed scale, leading to the utilization of implicit neural functions for generating arbitrarily scaled images.
We introduce a novel and efficient framework, the Mixture of Experts Implicit Super-Resolution (MoEISR), which enables super-resolution at arbitrary scales.
arXiv Detail & Related papers (2023-11-20T05:34:36Z) - Towards Real-World Burst Image Super-Resolution: Benchmark and Method [93.73429028287038]
In this paper, we establish a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to explore the faithful reconstruction of image details from multiple frames.
We also introduce a Federated Burst Affinity network (FBAnet) to investigate non-trivial pixel-wise displacement among images under real-world image degradation.
arXiv Detail & Related papers (2023-09-09T14:11:37Z) - ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised
Real-world Single Image Super-Resolution [60.90817228730133]
Single image super-resolution (SISR) is a challenging problem that aims to up-sample a given low-resolution (LR) image to a high-resolution (HR) counterpart.
Recent approaches are trained on simulated LR images degraded by simplified down-sampling operators.
We propose a novel Invertible scale-Conditional Function (ICF) which can scale an input image and then restore the original input with different scale conditions.
arXiv Detail & Related papers (2023-07-24T12:42:45Z) - DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image
Super-Resolution [15.694407977871341]
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation.
Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels.
We propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR.
arXiv Detail & Related papers (2022-12-15T04:34:57Z) - Best-Buddy GANs for Highly Detailed Image Super-Resolution [71.13466303340192]
We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input.
Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the SISR task.
We propose best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the immutable one-to-one constraint, we allow the estimated patches to dynamically seek the best supervision.
arXiv Detail & Related papers (2021-03-29T02:58:27Z) - 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) - 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.