利用深度残差学习实现单张图像超分辨率

AI Pub Date : 2024-03-21 DOI:10.3390/ai5010021
Moiz Hassan, K. Illanko, Xavier N Fernando
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引用次数: 0

摘要

单图像超分辨率(SSIR)是计算机视觉领域一个引人入胜的研究课题,其目标是利用创新技术从低分辨率图像中创建高分辨率图像。单幅图像超分辨率在医疗/卫星成像、远程目标识别和自动驾驶汽车等领域有着广泛的应用。与基于插值法的传统方法相比,深度学习技术因其卓越的性能和计算效率,最近在 SISR 领域备受关注。本文提出了一种基于自动编码器的深度学习模型,用于 SSIR。自动编码器的下采样部分主要使用 3 乘 3 卷积,没有子采样层。上采样部分使用转置卷积和下采样部分的残余连接。该模型使用 VILRC ImageNet 数据库和 RealSR 数据库的一个子集进行训练。在测试中,我们发现 PSNR 和 SSIM 等定量指标分别高达 76.06 和 0.93。我们还使用了感知质量等定性指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single Image Super Resolution Using Deep Residual Learning
Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/satellite imaging, remote target identification and autonomous vehicles. Compared to interpolation based traditional approaches, deep learning techniques have recently gained attention in SISR due to their superior performance and computational efficiency. This article proposes an Autoencoder based Deep Learning Model for SSIR. The down-sampling part of the Autoencoder mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose convolution and residual connections from the down sampling part. The model is trained using a subset of the VILRC ImageNet database as well as the RealSR database. Quantitative metrics such as PSNR and SSIM are found to be as high as 76.06 and 0.93 in our testing. We also used qualitative measures such as perceptual quality.
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