Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Noise reduction of image sequences using adaptive motion compensated frame averaging

Abstract: A scheme for noise reduction of image sequences by adaptively switching, on a block-by-block basis, between simple (nondisplaced) frame averaging and motion-compensated frame averaging is represented. The resulting noise reduction approaches that achievable with simple frame averaging, while maintaining the good image resolution achievable for motion compensated frame averaging. >

A New Adaptive Line Enhancer Based on Singular Spectrum Analysis

Abstract: Original adaptive line enhancer (ALE) is used for denoising periodic signals from white noise. ALE, however, relies mainly on second order similarity between the signal and its delayed version and is more effective when the signal is narrowband. A new ALE based on singular spectrum analysis (SSA) is proposed here. In this approach in the reconstruction stage of SSA, the eigentriples are adaptively selected (filtered) using the delayed version of the data. Unlike the conventional ALE where (second) order statistics are taken into account, here the full eigen-spectrum of the embedding matrix is exploited. Consequently, the system works for non-Gaussian noise and wideband periodic signals. By performing some experiments on synthetic signals it is demonstrated that the proposed system is very effective for separation of biomedical data, which often have some periodic or quasi-periodic components, such as EMG affected by ECG artefacts. This data are examined here.

Bearing fault diagnosis based on variational mode decomposition and total variation denoising

Abstract: Feature extraction plays an essential role in bearing fault detection. However, the measured vibration signals are complex and non-stationary in nature, and meanwhile impulsive signatures of rolling bearing are usually immersed in stochastic noise. Hence, a novel hybrid fault diagnosis approach is developed for the denoising and non-stationary feature extraction in this work, which combines well with the variational mode decomposition (VMD) and majoriation–minization based total variation denoising (TV-MM). The TV-MM approach is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the parameter is very important in TV-MM, the weighted kurtosis index is also proposed in this work to determine an appropriate used in TV-MM. The performance of the proposed hybrid approach is conducted through the analysis of the simulated and practical bearing vibration signals. Results demonstrate that the proposed approach has superior capability to detect roller bearing faults from vibration signals.

A note on multi-image denoising

Abstract: Taking photographs under low light conditions with a hand-held camera is problematic. A long exposure time can cause motion blur due to the camera shaking and a short exposure time gives a noisy image. We consider the new technical possibility offered by cameras that take image bursts. Each image of the burst is sharp but noisy. In this preliminary investigation, we explore a strategy to efficiently denoise multi-images or video. The proposed algorithm is a complex image processing chain involving accurate registration, video equalization, noise estimation and the use of state-of-the-art denoising methods. Yet, we show that this complex chain may become risk free thanks to a key feature: the noise model can be estimated accurately from the image burst. Preliminary tests will be presented. On the technical side, the method can already be used to estimate a non parametric camera noise model from any image burst.