Isabelle Bégin

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This paper addresses the problem of super-resolving a single image and recovering the characteristics of the sensor using a learning-based approach. In particular, the point spread function (PSF) of the camera is sought by minimizing the mean Euclidean distance function between patches from the input frame and from degraded versions of high-resolution(More)
This paper presents comparisons of two learning-based super-resolution algorithms as well as standard interpolation methods. To allow quality assessment of results, a comparison of a variety of image quality measures is also performed. Results show that a MRF-based super-resolution algorithm improves a previously interpolated image. The estimated degree of(More)
This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the camera. Second, we introduce a Point Spread(More)
This paper explores the possibility of assessing the adequacy of a training database to be used in a learning-based super-resolution process. The Mean Euclidean Distance (MED) function is obtained by averaging the distance between each input patch and its closest candidate in the training database, for a series of blurring kernels used to construct the(More)
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