Publications de Patrick Vandewalle

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Aliasing in images is often considered as a nuisance. Artificial low frequency patterns and jagged edges appear when an image is sampled at a too low frequency. However, aliasing also conveys useful information about the high frequency content of the image ...

In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the A/D converter, etc. A low-pass filter is then often applied before the sampling operation to avoid aliasing. However, when multiple copies are available, it is possible to use the information that is inherently present in the aliasing to reconstruct a higher resolution signal. If the different copies have unknown relative offsets, this is a non-linear problem in the off

The relation between sensor resolution and the optics of a digital camera is determined by the Nyquist sampling theorem: the sampling frequency should be larger than twice the maximum frequency of the image content coming out of the optical system. If a lower resolution is used, the output is aliased. Aliasing in digital images is often considered as a nuisance and (both optical and digital) filters are designed to avoid aliasing in digital cameras. However, aliasing also contains extra high-frequency infor

In signal processing systems, aliasing is normally treated as a disturbing signal. That motivates the need for effective analog, optical and digital anti-aliasing filters. However, aliasing conveys also valuable information on the signal above the Nyquist frequency. Hence, an effective processing of the samples, based on a model of the input signal, would allow to virtually increase the sampling frequency using slower and cheaper converters. In this paper, we present such an algorithm for bandlimited signal

We present a new method for signal reconstruction from multiple sets of samples with unknown offsets. We rewrite the reconstruction problem as a set of polynomial equations in the unknown signal parameters and the offsets between the sets of samples. Then, ...

It is well-known that taking portrait photographs with a built in camera may create a red-eye effect. This effect is caused by the light entering the subject’s eye through the pupil and reflecting from the retina back to the sensor. These red eyes are prob ...

In this paper, we present a super-resolution method to approximately double image resolution in both dimensions from a set of four low resolution, aliased images. The camera is shifted and rotated by small amounts between the different image captures. Only the low frequency, aliasing-free part of the images is used to find the shift and rotation parameters. When the images are registered, it is possible to reconstruct a higher resolution, aliasing-free image from the four low resolution images using cubic i

In this paper, we present a simple method to almost quadruple the spatial resolution of aliased images. From a set of four low resolution, undersampled and shifted images, a new image is constructed with almost twice the resolution in each dimension. The resulting image is aliasing-free. A small aliasing-free part of the frequency domain of the images is used to compute the exact subpixel shifts. When the relative image positions are known, a higher resolution image can be constructed using the Papoulis-Ger

Have you ever tried to reproduce the results presented in a research paper? For many of our current publications, this would unfortunately be a challenging task. For a computational algorithm, details such as the exact data set, initialization or terminati ...

If multiple images of a scene are available instead of a single image, we can use the additional information conveyed by the set of images to generate a higher quality image. This can be done along multiple dimensions. Super-resolution algorithms use a set ...

How often have you been able to implement an algorithm as it is described in a paper? And when you did, were you confident that you had exactly the same parameter values and results as the authors of the paper? All too often, articles do not describe all t ...

Super-resolution algorithms reconstruct a high resolution image from a set of low resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low resolution images are undersampled and have aliasing a ...

From image retrieval to image classification, all research shares one common requirement: a good image database to test or train the algorithms. In order to create a large database of images, we set up a project that allowed gathering a collection of more ...

Aliasing artifacts in images are visually very disturbing. Therefore, most imaging devices apply a low-pass filter before sampling. This removes all aliasing from the image, but it also creates a blurred image. Actually, all the image information above hal ...

We present a new algorithm that performs demosaicing and super-resolution jointly from a set of raw images sampled with a color filter array. Such a combined approach allows us to compute the alignment parameters between the images on the raw camera data b ...

Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domains such as HDTV, satellite imaging, and video surveillance. These techniques ...

In multichannel sampling, several sets of sub-Nyquist sampled signal values are acquired. The offsets between the sets are unknown, and have to be resolved, just like the parameters of the signal itself. This problem is nonlinear in the offsets, but linear ...

Super-resolution imaging techniques reconstruct a high resolution image from a set of low resolution images that are taken from almost the same point of view. The problem can be subdivided into two main parts: an image registration part where the different ...

Patrick Vandewalle

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Super-resolution from unregistered aliased images

Aliasing is Good for You: Joint Registration and Reconstruction for Super-Resolution

Super-Resolution from Unregistered and Totally Aliased Signals Using Subspace Methods

Registration for Super-Resolution: Theory, Algorithms, and Applications in Image and Mobile Video Enhancement

How to take advantage of aliasing in bandlimited signals

Signal Reconstruction from Multiple Unregistered Sets of Samples using Groebner Bases

Automatic Red-Eye Removal based on Sclera and Skin Tone Detection

Double resolution from a set of aliased images

Superresolution images reconstructed from aliased images

Reproducible Research in Signal Processing - What, why, and how

Multidimensional image enhancement from a set of unregistered differently exposed images

Experiences with Reproducible Research in Various Facets of Signal Processing Research

A Frequency Domain Approach to Registration of Aliased Images with Application to Super-Resolution

The Flux: Creating a Large Annotated Image Database

Super-Resolution from Highly Undersampled Images

Joint Demosaicing and Super-Resolution Imaging from a Set of Unregistered Aliased Images

Subspace-Based Methods for Image Registration and Super-Resolution

Groebner Basis Methods for Multichannel Sampling with Unknown Offsets

Registration of Aliased Images for Super-Resolution Imaging