Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g., superresolution from a single image). In this paper we show how this multi-scale property can also be used for “blind-deblurring”, namely, removal of an unknown blur from a blurry image. While patches repeat ‘as is’ across scales in a sharp natural image, t...

Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capa...

background : kernel smoothing method is a non-parametric or graphical method for statistical estimation. in the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. methods : by employing non-parametric regression methods, the curve estimation, may have some complexity.  in this article, four indices of epanechnikov, b...

It is known for decades that users tend to significantly underestimate or overestimate distances or speed in immersive virtual environments (IVEs) compared to corresponding judgments in the real world. Although several factors have been identified in the past that could explain small portions of this effect, the main causes of these perceptual discrepancies still remain elusive. One of the fact...

Blind image deblurring is a particularly challenging inverse problem where the blur kernel is unknown and must be estimated en route to recover the deblurred image. The problem is of strong practical relevance since many imaging devices such as cellphone cameras, must rely on deblurring algorithms to yield satisfactory image quality. Despite significant research effort, handling large motions r...

The quality of image restoration from degraded images is highly dependent upon a reliable estimate of blur. This paper proposes a blind blur estimation technique based on the low rank approximation of cepstrum. The key idea that this paper presents is that the blur functions usually have low ranks when compared with ranks of real images and can be estimated from cepstrum of degraded images. We ...

This thesis investigates the removal of spatially-variant blur from photographs degraded by camera shake, and the removal of large occluding objects from photographs of popular places. We examine these problems in the case where the photographs are taken with standard consumer cameras, and we have no particular information about the scene being photographed. Most existing deblurring methods mod...

Image deblurring has matured over the last decade; today, there are a wide range of deblurring algorithms that operate successfully in the wild. Yet, there are many applications — including telephoto and low-light photography — where camera shake produces a blur kernel that is large enough to cripple state-of-the-art deblurring algorithms. This failure can be attributed to the decreasing SNR at...

Small Area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  Small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  Generally, in some cases, small area estimation uses parametric modeling.  But in fact, a lot of models have no linear relationship between the small area average and the covariat...