Digital image super-resolution using adaptive interpolation based on Gaussian function
This paper presents a new approach to digital image super-resolution (SR). Image SR is currently a very active area of research because it is used in various applications. The proposed technique uses Gaussian edge directed interpolation to determine the precise weights of the neighboring pixels. The standard deviation of the interpolation window determines the value of the sigma ? for generating Gaussian kernels. Therefore, the proposed scheme adaptively applies different Gaussian kernels according to the computed standard deviation of the interpolation window. Laplacian is applied to the image generated by the Gaussian kernels to enhance the visual quality of the output image. It has the significant benefit of being isotropic i.e. invariant to rotation. These features of being isotropic not only resemble human visual perception but also respond to intensity variations equally in all directions for any kind of kernel. It highlights the discontinuities of high frequencies in the image generated by the Gaussian kernel and deemphasizes the regions with slowly varying luminance levels. It also recovers the background missing features while preserving the sharpness of the output image. The proposed scheme preserves geometrical regularities across the boundaries and smoothes intensities inside the high frequencies. It also maintains the textures inside geometrical regularities. Therefore, high resolution (HR) images produced by the proposed scheme contain intensity information very close to the original details of the low-resolution (LR) image i.e. edges, smoothness and texture information. Various evaluation metrics have been applied to compute the validity of the proposed technique. Extensive experimental comparisons with state-of-the-art zooming schemes validate the claim of the proposed technique of being superior. It produces high quality at the cost of low time complexity.
super-resolution gaussian kernel laplacian human visual perception digital image magnification gaussian sigma weighted interpolation