Multiframe demosaicing and super-resolution of color images

Abstract

In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a MAP estimation technique by minimizing a multi-term cost function. The L 1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.

An overview of recent remote sensing and GIS based research in ecological informatics

“…A variety of super-resolution analyses may be undertaken. In particular super-resolution restitution seeks to form a finer spatial resolution image which may then form the focus of interest (Farsiu et al, 2006;Ling et al, 2009) or super-resolution mapping which seeks to map at a sub-pixel scale (Foody et al, 2005). Super-resolution techniques have been shown able to increase the accuracy and realism of key features such as class boundaries (Foody et al, 2005; and provide useful information for ecological research.…”

Sub-pixel Layout for Super-Resolution with Images in the Octic Group

“…4 the accuracy could be very high. We believe that by using more complicated regularization terms (e.g., [8]) and modern robust methods (e.g., [18]), the reconstruction accuracy could be further improved under severe noise.…”

On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction

“…One useful application is the reconstruction of a high-resolution (HR) image from multiple shifted low-resolution (LR) images in super-resolution imaging [25][26][27][28]. Since the LR images may be shifted arbitrarily from each other, the image samples are non-uniformly sampled.…”

“…Using LPR, we can construct a HR image from the non-uniform spaced image samples. More information about super-resolution and image registration can be found in [25][26][27][28][29][30]. Here, we only focus on the application of the SK-LPR-RICI method to the HR imaging from LR images without blurring.…”