A signal processor for median-based algorithms

Vainio, Olli; Neuvo, Y.; Butner, S.E.

doi:10.1109/29.31294

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…Paper [31] is one of the pioneer on programmable ROF processor; however their application is limited to median filtering. Thanks to the DCRAM structure, the proposed ROF processor is flexible with the variation of the rank order r and algorithms.…”

Section: Comparison Of Existing Rof Architecturesmentioning

confidence: 99%

“…These architectures are fully pipelined for high throughput rate at the expense of latency, but require a large number of compare-swap units and registers. To reduce the hardware complexity, papers [8,12,14,15,[29][30][31][32] present lineararray structures to maintain samples in sorted order. For a sliding window of size N, the lineararray architectures consist of N processing elements and require three steps for each iteration: finding the proper location for new coming sample, discarding the eldest one, and moving samples between the newest and eldest one position.…”

Section: Introductionmentioning

confidence: 99%

“…Although the hardware complexity is reduced to O(N), they require a large latency for DI steps. Paper [31] further presents a micro-programmable processor for the implementations of the median-type filters. Paper [20] presents a parallel architecture using two-phase design to improve the operating speed.…”

Section: Introductionmentioning

confidence: 99%

“…However, the circuits are time-consuming and complex so that they cannot take full advantages of bit-sliced algorithm. Some papers claim that this type of algorithm is impractical for logic circuit implementation because of its exponential complexity [31]. Paper [21] uses an inverter as a voter for majority calculation.…”

Section: Introductionmentioning

confidence: 99%

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On VLSI design of rank-order filtering using DCRAM architecture

Lin

Dung

2008

Integration

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This paper addresses on VLSI design of rank-order filtering (ROF) with a maskable memory for realtime speech and image processing applications. Based on a generic bit-sliced ROF algorithm, the proposed design uses a special-defined memory, called the dual-cell random-access memory (DCRAM), to realize major operations of ROF: threshold decomposition and polarization. Using the memory-oriented architecture, the proposed ROF processor can benefit from high flexibility, low cost and high speed. The DCRAM can perform the bit-sliced read, partial write, and pipelined processing. The bit-sliced read and partial write are driven by maskable registers. With recursive execution of the bit-slicing read and partial write, the DCRAM can effectively realize ROF in terms of cost and speed. The proposed design has been implemented using TSMC 0.18 μm 1P6M technology. As shown in the result of physical implementation, the core size is 356.1 × 427.7μm 2 and the VLSI implementation of ROF can operate at 256 MHz for 1.8V supply.

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Section: Comparison Of Existing Rof Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On VLSI design of rank-order filtering using DCRAM architecture

Lin

Dung

2008

Integration

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“…For example, median hybrid filters suggested by Heinonen, and Neuvo [6,7], running medians with robust regression, analized by Davies et al [8], Qc-method-based filters proposed by Croux and Rousseeuw [9,10]. For audio signal elaboration and image processing, many realizations of efficient median filters have been proposed [11][12][13][14], even if usually in this field fast and powerful processors or DSPs are employed. Particularly interesting are the so called trimmed or winsorized mean filter, better described in the next section [15].…”

Section: Introductionmentioning

confidence: 97%

Sensor Signal Processing for Autonomous Wireless Sensors

Bicelli

Depari

Flammini

et al. 2008

2008 IEEE Instrumentation and Measurement Technology Conference

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Wireless Sensor Networks are nowadays a reality. However, a wireless node must be a truly autonomous system, i.e. it must embed its own power source, generally a battery pack. For this reason, it is ofmain concern to limit every source of wastedpower. In particular, nodes exploit low duty-cycle strategies to increase their life, spending most oftheir time in low power mode, turning off all devices within the system except a wakeup oscillator. However, in this way some time must be spent waiting for circuitries settling time, especially ifa high accuracy measurement is required. In this paper an efficient implementation of a filtering algorithm that is able to reduce this time is discussed. It is based on a hybrid combination of median and mean filter, joining advantages of both linear and non-linear filtering. A well tailored implementation has been experimentally tested and discussed.

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An overview of median and stack filtering

Coyle

Gallagher

Gabbouj

1992

Circuits Systems and Signal Process

108

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Abstract. Within the last two decades a small group of researchers has built a useful, nontrivial theory of nonlinear signal processing around the median-related filters known as rank-order filters, order-statistic filters, weighted median filters, and stack filters. This required significant effort to overcome the bias, both in education and research, toward linear theory, which has been dominant since the days of Fourier, Laplace, and "Convolute."We trace the development of this theory of nonlinear filtering from its beginnings in the study of noise-removal properties and structural behavior of the median filter to the recently developed theory of optimal stack filtering.The theory of stack filtering provides a point of view which unifies many different filter classes, including morphological filters, so it is discussed in detail. Of particular importance is the way this theory has brought together, in a single analytical framework, both the estimation-based and the structural-based approaches to the design of these filters.Some recent applications of median and stack filters are provided to demonstrate the effectiveness of this approach to nonlinear filtering. They include: the design of an optimal stack filter for image restoration; the use of vector median filters to attenuate impulsive noise in color images and to eliminate cross luminance and cross color in TV images; and the use of median-based filters for image sequence coding, reconstruction, and scan rate conversion in normal TV and HDTV systems.

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A signal processor for median-based algorithms

Cited by 20 publications

References 17 publications

On VLSI design of rank-order filtering using DCRAM architecture

On VLSI design of rank-order filtering using DCRAM architecture

Sensor Signal Processing for Autonomous Wireless Sensors

An overview of median and stack filtering

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