Single Image Super-Resolution Based on Gradient Pro?le Sharpness

In this paper, a novel image superresolution algorithm is proposed based on GPS (Gradient Profile Sharpness). GPS is an edge sharpness metric, which is extracted from two gradient description models, i.e. a triangle model and a Gaussian mixture model for the description of different kinds of gradient profiles. Then the transformation relationship of GPSs in different image resolutions is studied statistically, and the parameter of the relationship is estimated automatically. Based on the estimated GPS transformation relationship, two gradient profile transformation models are proposed for two profile description models, which can keep profile shape and profile gradient magnitude sum consistent during profile transformation. Finally, the target gradient field of HR (high resolution) image is generated from the transformed gradient profiles, which is added as the image prior in HR image reconstruction model. Extensive experiments are conducted to evaluate the proposed algorithm in subjective visual effect, objective quality, and computation time. The experimental results demonstrate that the proposed approach can generate superior HR images with better visual quality, lower reconstruction error and acceptable computation efficiency as compared to state-of-the-art works

  • Project Category : IEEE Projects
  • Project Year : 2015-2016
  • Department
  • B.E(Computer Science) , B.E(Information Technology), B.SC(CS), B.SC(IT), B.Tech, BCA, M.E(Computer Science), M.E(SOFTWATE ENGG), M.SC(CS), M.SC(IT&M), M.SC(IT), M.SC(SOFTWARE ENGG), M.Tech, MCA,
  • Domain
  • Image Processing,
  • Technology
  • ASP.Net, C#.Net, .Net, Image processing, J2EE, J2ME, Java, PHP,
  • Avilable city
  • Hyderabad,

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