The standard approach to edge detection is based on a model of edges as large step changes in intensity. This approach fails to reliably detect and localize edges in natural images where blur scale and contrast can vary over a broad range. The main problem is that the appropriate spatial scale for local estimation depends upon the local structure of the edge, and thus varies unpredictably over ...

We present the first systematic evaluation of the data terms for multi-frame super-resolution within a variational model. The various data terms are derived by permuting the order of the blur-, downsample-, and warp-operators in the image acquisition model. This yields six different basic models. Our experiments using synthetic images with known ground truth show that two models are preferable:...

In disciplines like fluorescence microscopy and astronomical imaging, the imaging process is based on detection of photons. Fluctuations in photon counting processes are described by Poisson statistics. In this paper, a new combined method based on steerable pyramids is proposed for the estimation of the degradation parameters (like noise and blur) and the restoration of photon-limited images. ...

The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learningbased method of estimating blur kernel under the ‘0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the b...

We present texture cache approximation as a method for using existing hardware on GPUs to eliminate costly global memory accesses. We develop a technique for using a GPU’s texture fetch units to generate approximate values, and argue that this technique is applicable to a wide variety of GPU kernels. Applying texture cache approximation to an image blur kernel on an NVIDIA 780GTX, we obtain a 1...

A method of counts-in-cells analysis of galaxy distribution is investigated with arbitrary smoothing functions in obtaining the galaxy counts. We explore the possiblity of optimizing the smoothing function, considering a series of m-weight Epanechnikov kernels. The popular top-hat and Gaussian smoothing functions are two special cases in this series. In this paper, we mainly consider the second...

The course can be split in two parts. In the implementation part, the kernel estimation process as described in [1] has been studied. The algorithm has been tested against synthetic and real data; and its performance has been discussed. In the reading part, a number of papers have been covered from the list of papers discussed in the graduate level course CPSC 505 “Image Understanding – I: Imag...

The point spread function (PSF) of a blurred image is often unknown a priori; the blur must first be identified from the degraded image data before restoring the image. Generalized cross-validation (GCV) is introduced to address the blur identification problem. The GCV criterion identifies model parameters for the blur, the image, and the regularization parameter, providing all the information ...