The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure. We present a scale space perspective on blind deblurring algorithms, and introduce a cascaded scale space formulation for blind deblurring. This new formulation suggests a natural approach robust to noise and small scale structures throu...

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...

We analyze a variational method for reconstructing a bar code signal from a blurry and noisy measurement. The bar code is modeled as a binary function with a finite number of transitions and a parameter controlling minimal feature size. The measured signal is the convolution of this binary function with a Gaussian kernel. In this work, we assume that the blur kernel is known and establish condi...

Contrary to common assumptions in the literature, the blur kernel corresponding to lens-effect blur has been demonstrated to be spatially-varying across the image plane. Existing models for the corresponding point spread function (PSF) are either parameterized and spatially-invariant, or spatially-varying but ad-hoc and discretelydefined. In this paper, we develop and present a novel, spatially...