A performance impact of Andrew’s Sine threshold for a robust regularized SRR based on ML framework
Keywords:SRR (Super-Resolution Reconstruction), DIR (Digital Image Reconstruction), DIP (Digital Image Processing), ML (Maximum Likelihood) Estimation, DSP (Digital Signal Processing)
One of the most successful Digital Image Reconstruction (DIR) techniques for increasing image resolution and improving image quality is the Super-Resolution Reconstruction (SRR), which is the procedure of integrating a collection of aliased low-resolution low-quality images to form a single high-resolution high-quality image. However, the mainstream SRR algorithms are too delicate to noisy environments because these mainstream SRR algorithms are often comprised by the ML (L1 or L2) estimation techniques thereby the new robust SRR algorithm, which is comprised by Andrew’s Sine norm, has been proposed for dealing with noisy environments. Because the performance of the new SRR algorithm heavily relies on this Andrew’s Sine norm soft-threshold parameter, resultantly, this paper aims to investigate the impact characteristic of this norm constant parameter on the novel SRR algorithm. In addition, multitudinous experiments (which are applied on two standard images: Lena image and Susie image) are simulated to make the extensive results under five noise models: noise free, additive Gaussian noise, multiplicative Gaussian noise, Poisson noise and Impulsive noise with several noise powers for demonstrating the relationship between the SRR performance (in PSNR) and Andrew’s Sine norm soft-threshold parameter under each noisy cases.
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