针对潜在特征的单幅图像超分辨率重建

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xin Wang, Jing-Ke Yan, Jing-Ye Cai, Jian-Hua Deng, Qin Qin, Yao Cheng
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引用次数: 0

摘要

单图像超分辨率(SISR)通常侧重于将各种降级的低分辨率(LR)图像复原为单一的高分辨率(HR)图像。然而,在 SISR 任务中,模型要同时保持高质量和快速采样,并保留细节和纹理特征的多样性,往往具有挑战性。这一挑战可能导致模型崩溃、重建的高分辨率图像缺乏丰富的细节和纹理特征以及模型采样耗时过长等问题。为了解决这些问题,本文提出了一种面向潜特征的扩散概率模型(LDDPM)。首先,我们设计了一种能够有效编码 LR 图像的条件编码器,减少了模型图像重建的解空间,从而提高了重建图像的质量。然后,我们采用归一化流和多模态对抗训练,从复杂的多模态分布中学习,对去噪分布进行建模。这样做可以在最少的采样步骤内提高生成建模能力。我们提出的模型与现有的 SISR 方法在主流数据集上进行的实验比较表明,我们的模型能重建更逼真的 HR 图像,并在多个评估指标上取得更好的性能,为解决 SISR 任务提供了一个全新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Super-resolution reconstruction of single image for latent features

Super-resolution reconstruction of single image for latent features

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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