Edge preserving super-resolution infrared image reconstruction based on L1-and L2-norms

Super-resolution （SR） is a widely used tech- nology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared （IR） SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry e...

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Bibliographic Details
Published inFrontiers of Optoelectronics (Online) Vol. 10; no. 2; pp. 189 - 194
Main Authors Dai, Shaosheng, Zhang, Dezhou, Cui, Junjie, Zhang, Xiaoxiao, Liu, Jinsong
Format Journal Article
LanguageEnglish
Published Beijing Higher Education Press 01.06.2017
Springer Nature B.V
Subjects
Algorithms
Biomedical Engineering and Bioengineering
Electrical Engineering
Engineering
Image quality
Image reconstruction
Image resolution
Infrared imagery
Measurement methods
Norms
Physics
Research Article
Signal to noise ratio
Similarity
Visual effects
Visual signals
reconstruction
infrared (IR) super-resolution (SR) image
high frequency layer
edge texture
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Summary:Super-resolution （SR） is a widely used tech- nology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared （IR） SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry edges. Traditional and advanced methods maintain the quantitative measures, but they mostly fail to preserve edge and visual quality. This paper proposes an algorithm based on high frequency layer features. This algorithm focuses on the IR image edge texture in the reconstruction process. Experimental results show that the mean gradient of the IR image reconstructed by the proposed algorithm increased by 1.5, 1.4, and 1.2 times than that of the traditional algorithm based on L1- norm, L2-norm, and traditional mixed norm, respectively. The peak signal-to-noise ratio, structural similarity index, and visual effect of the reconstructed image also improved.
Bibliography:Super-resolution （SR） is a widely used tech- nology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared （IR） SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry edges. Traditional and advanced methods maintain the quantitative measures, but they mostly fail to preserve edge and visual quality. This paper proposes an algorithm based on high frequency layer features. This algorithm focuses on the IR image edge texture in the reconstruction process. Experimental results show that the mean gradient of the IR image reconstructed by the proposed algorithm increased by 1.5, 1.4, and 1.2 times than that of the traditional algorithm based on L1- norm, L2-norm, and traditional mixed norm, respectively. The peak signal-to-noise ratio, structural similarity index, and visual effect of the reconstructed image also improved.
10-1029/TN
infrared （IR） super-resolution （SR） image,reconstruction, high frequency layer, edge texture
ISSN:2095-2759
2095-2767
DOI:10.1007/s12200-016-0659-3