New edge-directed interpolation

Abstract

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation

“…To confirm the results obtained from the proposed SR technique over the state-of-the-art image resolution enhancement techniques, the novel SR-WAFE-SR technique was compared with interpolation methods such as Bicubic interpolation [6], Lanczos interpolation [7] and New EdgeDirected Interpolation (NEDI) [8]. It was also compared with promising reconstruction methods that employ wavelet transform (WT), such as Wavelet Zero Padding (WZP) [31] and Demirel-Anbarjafari Super Resolution (DASR) [12].…”

“…This interpolation has better approximation capabilities than classical bicubic interpolation. In the literature, there are many edge-directed interpolation methods, for example, the new edged-directed interpolation (NEDI) [8] estimates the local covariance coefficients from an LR image, and then uses this covariance value to estimate and to adapt the linear interpolation between the LR pixels to reconstruct the HR image via the geometric duality among the covariances. The method [9] proposes a new edge-guided nonlinear interpolation technique through directional filtering and data fusion.…”

Generation of a Zoomed Stereo Video Using Two Synchronized Videos with Different Magnifications

“…The filter based methods estimate a high-resolution image from a single low-resolution image by compensating high-frequency components of the input image [17]- [19]. Although these methods are applicable to many scenes, in general, filter-based methods obtain less natural and more blurry results than ones by example-based methods as far as appropriate examples are used.…”

Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

“…Super-resolution methods are currently a very active area of research, as it allows the implementation of low-cost imaging sensors in hyperspectral spectrometers. Many methodologies have been applied to the super-resolution problem, such as SR from single image methods (Zhang, 2012), (Takeda, 2007), (Li, 2001), multi-image-based SR methods (Protter, 2009), (Akgun, 2005), (Chan, 2010), and learning-based SR methods (Freeman, 2002). However, these methods ignore the spectral information that is a crucial parameter for an optimal reconstruction of high resolution (HR) images.…”