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Superresolution and Demosaicing

The main function of this software tool is the implementation of several superresolution techniques in our published work, and the comparison of these to other work. The techniques implemented cover robust methods, dynamic color superresolution methods, and simultaneous demosaicing and resolution enhancement.
Some of the test superresolution datasets used in our papers.

Kernel Regression for Image Processing and Reconstruction

This is a command-line based software package for MATLAB, developed at our lab, which is capable of performing several general image processing tasks, including:

Some examples of the performance of these methods.

Local Orientation Estimation

Based on the work reported in our paper, the objective of this Matlab-based software is to produce a dense orientation field (unit vector field orthogonal to the gradient direction) that defines the local direction of edges and features in an image. This method can be used to produced vector fields from noisy fingerprint images, which can then be used to extract features for matching and retrieval. The method can also be used as a preprocessor for locally adaptive image filtering methods such as bandelets, and steering kernel regression (see above).

Regularized Kernel Regression-Based Deblurring (AKTV)

In this work, we extended the use of kernel regression for deblurring applications. In some earlier examples in the literature, such non-parametric deblurring was sub-optimally performed in two sequential steps, namely, denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior (Adaptive Kernel Total Variation -- AKTV) that generalizes some of the most popular regularization techniques in the literature.