A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise

Boo, K.J.; Bose, N.K.

doi:10.1109/76.678623

Cited by 49 publications

(22 citation statements)

References 16 publications

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“…In our simulations we used a separable 5-tap filter with a smooth transition around . 3 π, but other filter could be used, tuned to the amount of smoothness desired, or to computational constrains. This high pass frame is then squared and low pass filtered (we used a 7×7 tap separable linear filter).…”

Section: T He Proposed Algorithmmentioning

confidence: 99%

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Image de-noising by selective filtering based on double-shot pictures

Florêncio

2005

IEEE International Conference on Image Processing 2005

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The quality of photographs is often reduced by sensor noise. This was a problem with film cameras, and is still a problem with current CCD and CMOS sensors, particularly in low-lighting conditions. De-noising techniques do not always perform satisfactorily. Typical de-noise techniques reduce the sharpness of the image. In this paper we propose a new de-noising technique, which is based on a dual-shot technique. The proposed algorithm is based on selectively removing high frequencies that do not correlate well between the frames. The algorithm borrows from video processing noise-removal techniques, but the final picture is derived from filtering a single shot, avoiding doublecontouring and other artifacts that may happen with video techniques. While the decision is made based on both frames, the filtering itself is done using exclusively one of the frames. For this reason, the second (auxiliary) shot may be of much lower quality.

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Section: T He Proposed Algorithmmentioning

confidence: 99%

“…In many respects, these techniques are similar to noise-removal or pre-processing techniques used in video, where pre-processing involves a temporal-filter, based on motion-compensated frames [1][2][3]. Nevertheless, areas that are not perfectly motion compensated will introduce artifacts (or "phantoms).…”

Section: Introductionmentioning

confidence: 96%

Image de-noising by selective filtering based on double-shot pictures

Florêncio

2005

IEEE International Conference on Image Processing 2005

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“…Note that v i can describe both signal-dependent and -independent noise (Arce, 1991;Boo and Bose, 1998;. In many practical situations, noisy signals are often characterized by abrupt local changes.…”

Section: Video Filtering Fundamentalsmentioning

confidence: 99%

Adaptive video filtering framework

Lukáč

Fischer

Motyl

et al. 2004

Int J Imaging Syst Tech

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We present a new 3D adaptive filtering approach capable of detecting and removing impulsive noise in image/video sequences. The proposed method takes advantage of switching median schemes and robust lower-upper-middle (LUM) smoothing characteristics. Simulation studies reported in this article indicate that the proposed filtering scheme achieves an excellent trade-off between noise attenuation and detail preserving characteristics, and clearly outperforms previously introduced approaches in terms of subjective and objective image quality measures. Besides the filter analysis and the testing of its performance, an important part of this article discusses the filter implementation in Altera field programmable logic devices (FPLD). Simulation studies indicate that the proposed method can be efficiently implemented in hardware and is suitable for real-time image/ video processing applications.

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“…K. J. Boo and N. K. Bose proposed to use motion compensation (MC) with an approximated 3D Wiener filter [2] while A. J. Patti et. al.…”

Section: Introductionmentioning

confidence: 99%

Maximum a Posteriori Based (MAP-Based) Video Denoising VIA Rate Distortion Optimization

Chen

Fan

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

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In this paper, a maximum a posteriori based (MAP-based) video denoising algorithm is proposed. According to the Bayes rule, the MAP estimate is determined by two terms: noise conditional density model and priori conditional density model. Based on the assumptions that the noise satisfies Gaussian distribution and the priori model is measured by the bit rate, the MAP estimate can be expressed as a rate distortion optimization problem. In order to find a suitable lagrangian parameter, we re-write the problem as a constraint minimization problem by setting the rate as an objective function and the distortion as a constraint. In this way, we find that the lagrangian parameter is determined by the distortion constraint. Fixing the distortion constraint, we can get the optimal lagrangian parameter, which leads to the optimal denoising result. Some experiments are conducted to demonstrate the efficiency and effectiveness of the proposed method.

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A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise

Cited by 49 publications

References 16 publications

Image de-noising by selective filtering based on double-shot pictures

Image de-noising by selective filtering based on double-shot pictures

Adaptive video filtering framework

Maximum a Posteriori Based (MAP-Based) Video Denoising VIA Rate Distortion Optimization

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