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Image SR-based NLM and DCNN improved IBP with cubic B-spline
The Imaging Science Journal ( IF 0.871 ) Pub Date : 2020-04-02 , DOI: 10.1080/13682199.2020.1757294
S. Jothi Lakshmi 1 , P. Deepa 1
Affiliation  

ABSTRACT Image super-resolution (SR) techniques aim to estimate high-resolution (HR) image from low-resolution (LR) image. Existing SR method has slow convergence and recovery of high-frequency details are inaccurate. To overcome these issues, two algorithms have been proposed for image SR based on non-local means improved iterative back projection (NLM-IIBP), deep convolutional neural network improved iterative back projection (DCNN-IIBP) to produce high-resolution images with low noise, minimal blur by restoring high-frequency details. In NLM-IIBP denoised images have been interpolated using cubic B-spline interpolation and processed using IIBP based on guided bilateral method. NLM preserves the edges effectively, but does not consider high dimensional information and over smoothing during noise minimization. To further improve the resolution, NLM is replaced by DCNN. DCNN denoising method suppresses different noises at different noise levels. The proposed algorithms have been analysed and compared with existing approaches using various parameters to prove the effectiveness.

中文翻译:

基于图像 SR 的 NLM 和 DCNN 使用三次 B 样条改进 IBP

摘要 图像超分辨率 (SR) 技术旨在从低分辨率 (LR) 图像中估计高分辨率 (HR) 图像。现有的SR方法收敛速度慢,高频细节恢复不准确。为了克服这些问题,已经提出了两种基于非局部均值改进迭代反向投影(NLM-IIBP)的图像SR算法,深度卷积神经网络改进迭代反向投影(DCNN-IIBP)以产生具有低分辨率的高分辨率图像。噪声,通过恢复高频细节最小化模糊。在 NLM-IIBP 中,已使用三次 B 样条插值对去噪图像进行插值,并使用基于引导双边方法的 IIBP 进行处理。NLM 有效地保留了边缘,但在噪声最小化过程中没有考虑高维信息和过度平滑。为了进一步提高分辨率,NLM 被 DCNN 取代。DCNN去噪方法在不同的噪声水平下抑制不同的噪声。对所提出的算法进行了分析,并与使用各种参数的现有方法进行了比较,以证明其有效性。
更新日期:2020-04-02
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