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.

The trilateral filter for high contrast images and meshes

Abstract: We present a new, single-pass nonlinear filter for edge-preserving smoothing and visual detail removal for N dimensional signals in computer graphics, image processing and computer vision applications. Built from two modified forms of Tomasi and Manduchi's bilateral filter, the new "trilateral" filter smoothes signals towards a sharply-bounded, piecewise-linear approximation. Unlike bilateral filters or anisotropic diffusion methods that smooth towards piecewise constant solutions, the trilateral filter provides stronger noise reduction and better outlier rejection in high-gradient regions, and it mimics the edge-limited smoothing behavior of shock-forming PDEs by region nding with a fast min-max stack. Yet the trilateral filter requires only one user-set parameter, filters an input signal in a single pass, and does not use an iterative solver as required by most PDE methods. Like the bilateral filter, the trilateral filter easily extends to N-dimensional signals, yet it also offers better performance for many visual applications including appearance-preserving contrast reduction problems for digital photography and denoising polygonal meshes.

Real Image Denoising With Feature Attention

Abstract: Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, its performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of the denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.

Crosstalk reduction for VLSI

Abstract: The performance of high-speed electronic systems is limited by interconnect-related failure modes such as coupled noise. We propose new techniques for alleviating the problems caused by coupling between signal lines on integrated circuits. We show that models used by previous work on coupled noise-constrained layout synthesis do not allow the use of several important degrees of freedom. These degrees of freedom include the ability to utilize dynamic noise margins rather than static noise margins, the dependence of coupled noise on drive strength, and the possibility of using overlaps to reduce susceptibility to noise. We derive an expression for the coupled noise integral and a bound for the peak coupled noise voltage which shows order of magnitude improvements in both accuracy and fidelity compared to the charge sharing model used in previous work. We use the new bounds to guide a greedy channel router, which manipulates exact adjacency information at every stage, allowing it to introduce jogs or doglegs when necessary for coupled noise reduction. Experimental results indicate that our algorithm compares favorably to previous work. The coupled noise is significantly reduced on benchmark instances.

Audio Signal Processing for Next-Generation Multimedia Communication Systems

Abstract: Preface. Contributing Authors. 1: Introduction Yiteng (Arden) Huang, J. Benesty. 1. Multimedia Communications. 2. Challenges and Opportunities. 3. Organization of the Book. I: Speech Acquisition and Enhancement. 2: Differential Microphone Arrays G.W. Elko. 1. Introduction. 2. Differential Microphone Arrays. 3. Array Directional Gain. 4. Optimal Arrays for Isotropic Fields. 5. Design Examples. 6. Sensitivity to Microphone Mismatch and Noise. 7. Conclusions. 3: Spherical Microphone Arrays for 3D Sound Recording J. Meyer, G.W. Elko. 1. Introduction. 2. Fundamental Concept. 3. The Eigenbeamformer. 4. Modal-Beamformer. 5. Robustness Measure. 6. Beampattern Design. 7. Measurements. 8. Summary. 9. Appendix A. 4: Subband Noise Reduction Methods for Speech Enhancement E.J. Diethorn. 1. Introduction. 2. Wiener Filtering. 3. Speech Enhancement by Short-Time Spectral Modification. 4. Averaging Techniques for Envelope Estimation. 5. Example Implementation. 6. Conclusion. II: Acoustic Echo Cancellation. 5: Adaptive Algorithms for MIMO Acoustic Echo Cancellation J. Benesty, T. Gansler, Yiteng (Arden) Huang, M. Rupp. 1. Introduction. 2. Normal Equations and Identification of a MIMO System. 3. The Classical and Factorized Multichannel RLS. 4. The Multichannel Fast RLS. 5. TheMultichannel LMS Algorithm. 6. The Multichannel APA. 7. The Multichannel Exponentiated Gradient Algorithm. 8. The Multichannel Frequency-domain Adaptive Algorithm. 9. Conclusions. 6: Double-talk Detectors for Acoustic Echo Cancellers T. Gansler, J. Benesty. 1. Introduction. 2. Basics of AEC and DTD. 3. Double-talk Detection Algorithms. 4. Comparison of DTDs by Means of the ROC. 5. Discussion. 7: The WinEC: A Real-Time Hands-Free Stereo Communication System T. Gansler, V. Fischer, E.J. Diethorn, J. Benesty. 1. Introduction. 2. System Description. 3. Algorithms of the Echo Canceller Module. 4. Residual Echo and Noise Suppression. 5. Simulations. 6. Real-Time Tests with Different Modes of Operation. 7. Discussion. III: Sound Source Tracking and Separation. 8: Time Delay Estimation Jingdong Chen, Yiteng (Arden) Huang, J. Benesty. 1. Introduction. 2. Signal Models. 3. Generalized Cross-Correlation Method. 4. The Multichannel Cross-Correlation Algorithm. 5. Adaptive Eigenvalue Decomposition Algorithm. 6. Adaptive Multichannel Time Delay Estimation. 7. Experiments. 8. Conclusions. 9: Source Localization Yiteng (Arden) Huang, J. Benesty, G.W. Elko. 1. Introduction. 2. Source Localization Problem. 3. Measurement Model and Cramer-Rao lower Bound for Source Localization. 4. Maximum Liklihood Estimator. 5. Least Squares Estimate. 6. Example

Adaptive microphone-array system for noise reduction

Abstract: This paper introduces a new adaptive microphone-array system for noise reduction (AMNOR system). It is first shown that there exists a tradeoff relationship between reducing the output noise power and reducing the frequency response degradation of a microphone-array to a desired signal. It is then shown that this tradeoff can be controlled by the introduction of a fictitious desired signal. A new optimization criterion is presented which minimizes the output noise power while maintaining the frequency response degradation below some pre-determined value (AMNOR criterion). AMNOR determines an optimal noise reduction filter based on this criterion by controlling the tradeoff utilizing the fictitious desired signal. Experiments on noise reduction processing were carried out in a room with a 0.4-s reverberation time. The superiority of the AMNOR criterion over conventional LMS and constrained LMS criteria for reducing noise in speech signals was confirmed in subjective preference tests. The AMNOR system improved the SNR by more than 15 dB in the 300-3200 Hz range.