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Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion,...
We study the problem of recovering point sources from samples of their convolution with a Gaussian kernel, showing that a convex program achieves exact deconvolution as long as the sources are not too clustered together and there are at least two samples close to the location of each source. The result is established using a novel dual-certificate construction.
Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input...
Shift-invariant approximation plays an important role in topics related with sampling theory. Despite being a theoretically mature topic, when it comes to practice, splines and Gaussians are the only well known generators. Here, we show that kernels introduced by Vallée Poussin, in context of the singular convolution integrals, can be successfully repurposed for shift-invariant approximation theory...
The geometric remapping of pixel values during the processing of digital imagery, such as magnification, warping and registration, can significantly affect the final image quality. Many medical imaging systems include a resampler/interpolator, such as bicubic, as part of their processing, that acts as a variable low pass filter. This not only degrades the spatial frequency response of the image and...
Kernel density estimation is a popular method for identifying crime hotspots for the purpose of data-driven policing. However, computing a kernel density estimate is computationally intensive for large crime datasets, and the quality of the resulting estimate depends heavily on parameters that are difficult to set manually. Inspired by methods from image processing, we propose a novel way for performing...
Cooperative localization capability is a highly desirable characteristic of wireless sensor networks. It has attracted considerable research attention in academia and industry. The sum-product algorithm over a wireless sensor network (SPAWN) is a powerful method to cooperatively estimate the positions of many sensors (agents) using knowledge of the absolute positions of a few sensors (anchors). Drawbacks...
This paper will be shown the problem and solve of the image interpolation by the directional inverse distance weighting (IDW). The anti-alias method which is the blurring kernel is used for solving on the IDW method. The problem of this method has occurred after this method is processed finish. The experiment results are shown the better performance than the conventional method.
Resampling is a necessary step at the interface of systems that require different sampling rates. Several techniques have been proposed in the literature to perform resampling. We perform a comparative study of the results of some interpolation and resampling techniques for speech and audio signals. The techniques are compared on the basis of quality of reconstruction and computational cost. We consider...
We propose the technique of the semi-automatic image creation. By this we mean an automatic completion of an image that is partially defined on the given domain. The essential feature of this technique is that the complementary area is much larger than that where the image is defined. Moreover, the proposed technique can be used in image upsampling, image inpainting, etc. In this contribution, we...
We propose a low rank structured matrix completion algorithm for image inpainting problems originated from scanning microscopy. The proposed method exploits the annihilation property observed in Gaussian Markov Random Field (GMRF) or partial differential equation (PDE)-based inpainting approaches. By utilizing the commutative property of the convolution, the annihilation property is embodied into...
Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations...
High performance and energy efficient video analytics systems that can extract rich metadata from voluminous visual content, will enable a variety of high-value surveillance, driver assistance, video tagging, and first person analytics systems. These big-data applications are pervasive across retail, automotive, medical, agriculture and security domains. However, current trends in general purpose...
Among existing interpolation methods, convolution-based methods are able to perform arbitrary factor interpolation but the results are usually blurry or jaggy, adaptive interpolation methods usually can reduce the blurry and jaggy artifacts but cannot handle arbitrary factor interpolation. In this paper we propose an arbitrary factor adaptive interpolation algorithm by combining 2-D piecewise autoregressive...
The Non-Uniform Fast Fourier Transform (NUFFT) is a generalization of FFT to non-equidistant samples. It has many applications which vary from medical imaging to radio astronomy to the numerical solution of partial differential equations. Despite recent advances in speeding up NUFFT on various platforms, its practical applications are still limited, due to its high computational cost, which is significantly...
For interventional monitoring, we aim at 4D ultrasound reconstructions of structures in the beating heart from 2D transesophageal echo images by fast scan plane rotation, unsynchronized to the heart rate. For such sparsely and irregularly sampled 2D images, a special spatiotemporal interpolation approach is desired. We have previously shown the potential of spatiotemporal interpolation by normalized...
Image deblurring is essential to high resolution imaging and is therefore widely used in astronomy, microscopy or computational photography. While shift-invariant blur is modeled by convolution and leads to fast FFT-based algorithms, shift-variant blurring requires models both accurate and fast. When the point spread function (PSF) varies smoothly across the field, these two opposite objectives can...
With the increasing use of digital monitor technique, requirements for image interpolation have become more critical. Especially, the development of LCD screen sizes has grown up. Conventional interpolation methods have several drawbacks, such as blurring or blocky effects. Many edge detect methods have been widely used to avoid these problems but are notorious for high complexity and cost. Therefore,...
In this paper, successfully we had implemented Fant's resampling algorithm by using cubic convolution for resampling the images that consists two passes. It does not exhibit the aliasing artifacts associated with techniques for spatial transform of discrete sampled images is possible through the use of a complete and continuous resampling interpolation algorithm. Applications to image processing include...
It is possible to correct intensity inhomogeneity in fat-water Magnetic Resonance Imaging (MRI) by estimating a bias field based on the observed intensities of voxels classified as the pure adipose tissue. The same procedure can also be used to quantify fat volume and its distribution which opens up for new medical applications. The bias field estimation method has to be robust since pure fat voxels...
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