An FPGA-Oriented Algorithm for Real-Time Filtering of Poisson Noise in Video Streams, with Application to X-Ray Fluoroscopy

Castellano, G.; De, Davide; Esposito, Darjn; Bifulco, Paolo; Napoli, Ettore; Petra, Nicola; Andreozzi, Emilio; Cesarelli, Mario; Strollo, A.G.M.

doi:10.1007/s00034-018-01020-x

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…As expected, computational times for NVCA filters resulted just few percent of that required for the VBM4D. Hardware implementations (such as [22, 23]) can further reduce the computational time using a lookup table instead of calculating the noise-threshold pixel by pixel.…”

Section: Resultsmentioning

confidence: 61%

“…This approach helps to preserve the object edges both in space and over time. In the hardware implementation of the NVCA filter [22, 23], the predetermined T thresholds corresponding to the entire range of the image gray levels were stored into a lookup table to guarantee a faster computation.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, the block-matching processes permit to track and reallocate moving objects for a deep noise reduction and edge preservation. Because of these advantages VBM4D is a reference, state-of-the-art video denoiser [19, 23, 30–32, 39, 48]. The average filters (as the common fluoroscopy time-average pre-processor) allow fast and effective quantum noise suppression but they causes severe edge degradation and motion blur.…”

Section: Methodsmentioning

confidence: 99%

“…The NVCA provides for a single division operation per pixel [17, 21–23], so for the same frame of N -by- N pixels, it has a computational complexity order of O ( N 2 ), which results several orders of magnitude lower than VBM4D’s one.…”

Section: Methodsmentioning

confidence: 99%

“…The Noise Variance Conditioned Average (NVCA) algorithm [17], which considers the specific signal-dependent, Poisson-distributed nature of the quantum noise, has proven to be more efficient than several algorithms in the denoising of X-ray images [21], while keeping the computational burden low enough to allow real-time hardware implementations [22, 23].…”

Section: Introductionmentioning

confidence: 99%

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Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

Sarno

Andreozzi

et al. 2019

BioMed Eng OnLine

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Background Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient’s dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too. Results For NVCA filter mask size of 5 × 5 × 5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter. Conclusions The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient’s X-ray dose.

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Section: Resultsmentioning

confidence: 61%

Section: Methodsmentioning

confidence: 99%