Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

image reconstruction from incomplete data

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Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying spa...

The Analysis of Adaptive Data Collection Methods for Machine Learning

Over the last decade the machine learning community has watched the size and complexity of datasets grow at an exponential rate, with some describing the phenomenon as big data. There are two main bottlenecks for the performance of machine learning methods: computational resources and the amount of labelled data, often provided by a human expert. Advances in distributed computing and the advent...