Reconstruction filter

About: Reconstruction filter is a research topic. Over the lifetime, 768 publications have been published within this topic receiving 12152 citations. The topic is also known as: Anti-imaging filter.

Introduction to signal processing

Abstract: 1. Sampling and Reconstruction. 2. Quantization. 3. Discrete-Time Systems. 4. FIR Filtering and Convolution. 5. z-Transforms. 6. Transfer Functions. 7. Digital Filter Realizations. 8. Signal Processing Applications. 9. DFT/FFT Algorithms. 10. FIR Digital Filter Design. 11. IIR Digital Filter Design. 12. Interpolation, Decimation, and Oversampling. 13. Appendices. References. Index.

Plenoptic sampling

Abstract: This paper studies the problem of plenoptic sampling in image-based rendering (IBR). From a spectral analysis of light field signals and using the sampling theorem, we mathematically derive the analytical functions to determine the minimum sampling rate for light field rendering. The spectral support of a light field signal is bounded by the minimum and maximum depths only, no matter how complicated the spectral support might be because of depth variations in the scene. The minimum sampling rate for light field rendering is obtained by compacting the replicas of the spectral support of the sampled light field within the smallest interval. Given the minimum and maximum depths, a reconstruction filter with an optimal and constant depth can be designed to achieve anti-aliased light field rendering.Plenoptic sampling goes beyond the minimum number of images needed for anti-aliased light field rendering. More significantly, it utilizes the scene depth information to determine the minimum sampling curve in the joint image and geometry space. The minimum sampling curve quantitatively describes the relationship among three key elements in IBR systems: scene complexity (geometrical and textural information), the number of image samples, and the output resolution. Therefore, plenoptic sampling bridges the gap between image-based rendering and traditional geometry-based rendering. Experimental results demonstrate the effectiveness of our approach.

Generating antialiased images at low sampling densities

Abstract: Ray tracing produces point samples of an image from a 3-D model Constructing an antialiased digital picture from point samples is difficult without resorting to extremely high sampling densities This paper describes a program that focuses on that problem While it is impossible to eliminate aliasing totally, it has been shown that nonuniform sampling yields aliasing that is less conspicuous to the observer An algorithm is presented for fast generation of nonuniform sampling patterns that are optimal in some sense Some regions of an image may require extra sampling to avoid strong aliasing Deciding where to do extra sampling can be guided by knowledge of how the eye perceives noise as a function of contrast and color Finally, to generate the digital picture, the image must be reconstructed from the samples and resampled at the display pixel rate The nonuniformity of the samples complicates this process, and a new nonuniform reconstruction filter is presented which solves this problem efficiently

A dual-tree complex wavelet transform with improved orthogonality and symmetry properties

Abstract: We present a new form of the dual-tree complex wavelet transform (DT CWT) with improved orthogonality and symmetry properties. Beyond level 1, the previous form used alternate odd-length and even-length bi-orthogonal filter pairs in the two halves of the dual-tree, whereas the new form employs a single design of even-length filter with asymmetric coefficients. These are similar to the Daubechies orthonormal filters, but designed with the additional constraint that the filter group delay should be approximately one quarter of the sample period. The filters in the two trees are just the time-reverse of each other, as are the analysis and reconstruction filters. This leads to a transform, which can use shorter filters, which is orthonormal beyond level 1, and in which the two trees are very closely matched and have a more symmetric sub-sampling structure, but which preserves the key DT CWT advantages of approximate shift-invariance and good directional selectivity in multiple dimensions.