NDLI: Edge preservation image enlargement and enhancement method based on the adaptive Perona

Content Provider	IET Digital Library
Author	Maiseli, Baraka Elisha, Ogada Mei, Jiangyuan Gao, Huijun
Abstract	In this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super-resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.
Starting Page	753
Ending Page	760
Page Count	8
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 12, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0040
Journal	IET Image Processing
Publisher Date	2014-05-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Peronaâ€“Malik Nonlinear Diffusion Model Computer Vision And Image Processing Technique Diffusion Diffusivity Component Edge Detection Edge Preservation Edge Sharpening Feature Distortion Feature Extraction Homogeneous Region Image Enhancement Image Enhancement Method Image Enlargement Image Recognition Image Reconstruction Image Resolution Image Topography Regularisation Kernel SR Model Superresolution Model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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