Edge Preservation Image Enlargement and Enhancement Method Based on the Adaptive Perona–Malik Non-Linear Diffusion Model
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Date
2014
Journal Title
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Publisher
IEEE
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.
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Citation
Maiseli, B., Elisha, O., Mei, J. and Gao, H., 2014. Edge preservation image enlargement and enhancement method based on the adaptive Perona–Malik non-linear diffusion model. IET Image Processing, 8(12), pp.753-760.