| Patent application number | Description | Published |
|---|
| 20080247659 | Universal-denoiser context-modeling component and context-modeling method - In various embodiments of the present invention, a context-based denoiser is applied to each noisy-image symbol embedded within a context to determine a replacement symbol for the noisy-signal symbol. The context-based denoiser includes a context-modeling component that efficiently generates context classes and symbol-prediction classes, assigns individual contexts to context classes and symbol-prediction classes, collects symbol-occurrence statistics related to the generated context classes and symbol-prediction classes, and, optionally, generates noisy-symbol predictions. | 10-09-2008 |
| 20090037795 | Denoising and Error Correction for Finite Input, General Output Channel - Systems and methods are disclosed for denoising for a finite input, general output channel. In one aspect, a system is provided for processing a noisy signal formed by a noise-introducing channel in response to an error correction coded input signal, the noisy signal having symbols of a general alphabet. The system comprises a denoiser and an error correction decoder. The denoiser generates reliability information corresponding to metasymbols in the noisy signal based on an estimate of the distribution of metasymbols in the input signal and upon symbol transition probabilities of symbols in the input signal being altered in a quantized signal. A portion of each metasymbol provides a context for a symbol of the metasymbol. The quantized signal includes symbols of a finite alphabet and is formed by quantizing the noisy signal. The error correction decoder performs error correction decoding on noisy signal using the reliability information generated by the denoiser. | 02-05-2009 |
| 20100278447 | Method and system for adaptive context-embedded prediction - One embodiment of the present invention is directed to an adaptive context-based predictor that predicts a value {circumflex over (x)} from a context, stored in an electronic memory, corresponding to a noisy-dataset symbol z | 11-04-2010 |
| 20110026848 | METHOD AND SYSTEM FOR ROBUST UNIVERSAL DENOISING OF NOISY DATA SETS - Embodiments of the present invention provide context-class-based universal denoising of noisy images and other noise-corrupted data sets. Prediction-error statistics for each prediction class, relative to a prefiltered image, are collected to estimate a bias for each prediction class, and prediction-error statistics for each conditioning class, relative to a prefiltered image, are accumulated based on the difference between predicted values and corresponding prefiltered-image symbols. The prediction-error statistics are accumulated using computed prediction-error-statistics vectors, with inversion of a prediction-error vector generated from each prediction prior to accumulation in a prediction-error-statistics vector. Conditional probability distributions are computed for individual contexts, which allow for computing a clean-image-estimated, value for each noisy-image value by minimizing a computed distortion over a range of possible estimated-clean-image symbols. | 02-03-2011 |
| 20110026850 | CONTEXT-CLUSTER-LEVEL CONTROL OF FILTERING ITERATIONS IN AN ITERATIVE DISCRETE UNIVERSAL DENOISER - Embodiments of the present invention are directed to various enhanced discrete-universal denoisers that have been developed to denoise images and other one-dimensional, two-dimensional or higher-dimensional data sets in which the frequency of occurrence of individual contexts may be too low to gather efficient statistical data or context-based symbol prediction. In these denoisers, image quality, signal-to-noise ratios, or other measures of the effectiveness of denoising that would be expected to increase monotonically over a series of iterations may decrease, due to assumptions underlying the discrete-universal-denoising method losing validity. Embodiments of the present invention apply context-class-based statistics and statistical analysis to determine, on a per-context-class basis, when to at least temporarily terminate denoising iterations on each conditioning class. Each iteration of the iterative methods applies context-based denoising only for those conditioning classes that statistical analysis indicates remain valid for denoising purposes. | 02-03-2011 |
| 20110129046 | DISCRETE DENOISING USING BLENDED COUNTS - Various embodiments of the present invention relate to a discrete denoiser that replaces symbols in a received, noisy signal with replacement symbols in order to produce a recovered signal less distorted with respect to an originally transmitted, clean signal than the received, noisy signal. Certain, initially developed discrete denoisers employ an analysis of the number of occurrences of metasymbols within the received, noisy signal in order to select symbols for replacement, and to select the replacement symbols for the symbols that are replaced. Denoisers that represent examples of the present invention use blended counts that are combinations of the occurrences of metasymbol families within a noisy signal to determine the symbols to be replaced and the replacement symbols corresponding to them. | 06-02-2011 |
| 20110298610 | COMPRESSING DATA IN A WIRELESS NETWORK - A distinguished node is dynamically selected from a subset of nodes in a wireless network. Data samples from the subset of nodes are received in view of the distinguished node status. At least one estimate is generated from the data samples and the data samples are compressed conditioned on the estimate. | 12-08-2011 |
| 20110299455 | COMPRESSING DATA IN A WIRELESS MULTI-HOP NETWORK - A first node receives aggregated compressed data and unaggregated data from a second node in a wireless multi-hop network. The first node compresses its own collected data based on the received unaggregated data. The first node aggregates its own compressed data with the aggregated compressed data received from the second node. The first node forwards an unaggregated version of its own collected data along with aggregated compressed data to a next hop in the wireless multi-hop network. | 12-08-2011 |
