Computational Imaging and Sensing: Theory and Applications
Abstract.
The revolution in sensing, with the emergence of many new imaging techniques, offers the possibility of gaining unprecedented access to the physical world, but this revolution can only bear fruit through the skilful interplay between the physical and computational realms. This is the domain of computational imaging which advocates that, to develop effective imaging systems, it will be necessary to go beyond the traditional decoupled imaging pipeline where device physics, image processing and the end-user application are considered separately. Instead, we need to rethink imaging as an integrated sensing and inference model. In the first part of the talk we highlight the centrality of sampling theory in computational imaging and investigate new sampling modalities which are inspired by the emergence of new sensing mechanisms. We discuss time-based sampling which is connected to event-based cameras where pixels behave like neurons and fire when an event happens. We derive sufficient conditions and propose novel algorithms for the perfect reconstruction of classes of non-bandlimited functions from time-based samples. We then develop the interplay between learning and computational imaging and present a model-based neural network for the reconstruction of video sequences from events. The architecture of the network is model-based and is designed using the unfolding technique, some element of the acquisition device are part of the network and are learned with the reconstruction algorithm. In the second part of the talk, we focus on the heritage sector which is experiencing a digital revolution driven in part by the increasing use of non-invasive, non-destructive imaging techniques. These new imaging methods provide a way to capture information about an entire painting and can give us information about features at or below the surface of the painting. We focus on Macro X-Ray Fluorescence (XRF) scanning which is a technique for the mapping of chemical elements in paintings and introduce a method that can process XRF scanning data from paintings. The results presented show the ability of our method to detect and separate weak signals related to hidden chemical elements in the paintings. We analyse the results on Leonardo's 'The Virgin of the Rocks' and show that our algorithm is able to reveal, more clearly than ever before, the hidden drawings of a previous composition that Leonardo then abandoned for the painting that we can now see. This is joint work with R. Alexandru, R. Wang, Siying Liu, J. Huang and Y.Su from Imperial College London; C. Higgitt and N. Daly from The National Gallery in London and Thierry Blu from the Chinese University of Hong Kong.