Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

An algorithm for integrated noise reduction and sharpness enhancement

Abstract: Noise reduction and image sharpening are techniques to improve video image quality. However, noise filters tend to blur image detail, while filters for image sharpening tend to increase noise. So, cascading the two filters does not always give the best performance. We present an integrated filter that reduces noise and sharpens details in a noisy video signal depending on local image statistics. This allows both features to be maximally exploited.

Astronomical image and signal processing: looking at noise, information and scale

Abstract: We present methods used to measure the information in an astronomical image, in both a statistical and a deterministic way. We discuss the wavelet transform and noise modeling, and describe how to measure the information and the implications for object detection, filtering, and deconvolution. The perspectives opened up by the range of noise models, catering for a wide range of eventualities in physical science imagery and signals, and the new two-pronged but tightly coupled understanding of the concept of information have given rise to better quality results in applications such as noise filtering, deconvolution, compression, and object (feature) detection. We have illustrated some of these new results in this article. The theoretical foundations of our perspectives have been sketched out. The practical implications, too, are evident from the range of important signal processing problems which we can better address with this armoury of methods. The results described in this work are targeted at information and at relevance. While we have focused on experimental results in astronomical image and signal processing, the possibilities are apparent in many other application domains.

Adaptive fuzzy control of satellite attitude by reinforcement learning

Abstract: The attitude control of a satellite is often characterized by a limit cycle, caused by measurement inaccuracies and noise in the sensor output. In order to reduce the limit cycle, a nonlinear fuzzy controller was applied. The controller was tuned by means of reinforcement learning without using any model of the sensors or the satellite. The reinforcement signal is computed as a fuzzy performance measure using a noncompensatory aggregation of two control subgoals. Convergence of the reinforcement learning scheme is improved by computing the temporal difference error over several time steps and adapting the critic and the controller at a lower sampling rate. The results show that an adaptive fuzzy controller can better cope with the sensor noise and nonlinearities than a standard linear controller.

Tensor Voting Guided Mesh Denoising

Abstract: Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS) This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting Our framework is direct, practical, and easy to understand Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression