Adam Scholefield

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It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of each sample is known and that the only distortion present in the observations is due to additive measurement noise. However, there are some applications where such location(More)
Although many advances have been made in light-field and camera-array image processing, there is still a lack of thorough analysis of the localisation accuracy of different multi-camera systems. By considering the problem from a frame-quantisation perspective, we are able to quantify the point localisation error of circular camera configurations.(More)
Iterative shrinkage of sparse and redundant representations are at the heart of many state of the art denoising and deconvolution algorithms. They assume the signal is well approximated by a few elements from an overcomplete basis of a linear space. If one instead selects the elements from a nonlinear manifold it is possible to more efficiently represent(More)
The success of many image restoration algorithms is often due to their ability to sparsely describe the original signal. Shukla proposed a compression algorithm, based on a sparse quadtree decomposition model, which could optimally represent piecewise polynomial images. In this paper, we adapt this model to the image restoration by changing the(More)
Techniques based on sparse and redundant representations are at the heart of many state of the art denoising and deconvolution algorithms. A very sparse representation of piecewise polynomial images can be obtained by using a quadtree decomposition to adaptively select a basis. We have recently exploited this to restore images of this form, however the same(More)
—In this paper, we investigate point localisation using multi-camera systems consisting of cameras with a finite number of pixels, under the assumption of known camera poses and error-free feature matching. We prove that, for any 2D multi-camera system and triangulation algorithm, the average localisation performance cannot asymptotically decay faster than(More)
Could bandwidth—one of the most classic concepts in signal processing—have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our attention to orthographic projection and assume a 2-D world.
We propose a novel camera pose estimation or perspective-n-point (PnP) algorithm, based on the idea of consistency regions and half-space intersections. Our algorithm has linear time-complexity and a squared reconstruction error that decreases at least quadratically, as the number of feature point correspondences increase., Inspired by ideas from(More)
It often happens that we are interested in reconstructing a signal from partial measurements. In Unlabeled sensing problem, we consider the case in which the order of noisy samples out of a linear measurement system is missing. Thus, the main challenge would be: given a set of unordered sample values, how to recover the data uniquely and at the same time,(More)
Accurate registration is critical to most multi-channel signal processing setups, including image super-resolution. In this paper we use modern sampling theory to propose a new robust registration algorithm that works with arbitrary sampling kernels. The algorithm accurately approximates continuous-time Fourier coefficients from discrete-time samples. These(More)