Context based super resolution image reconstruction

In this paper a context based super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator identifies local gradients and textures for selecting the optimal SR method for the region of interest. Texture segmentation and gradient map estimation are done prior to the reconstruction stage. Gradient direction is used for optimal noise reduction along the edges for non-textured regions. On the other hand, regularization term is cancelled for textured regions so that the resultant method reduces to maximum likelihood (ML) solution. It is demonstrated on Brodatz Texture Database that ML solution gives the best PSNR values on textures compared to the regularized SR methods in the literature. Experimental results show that the proposed hybrid method has superior performance in terms of Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index Measure (SSIM) compared the SR methods in the literature.

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