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.

Twin-image noise reduction by phase retrieval in in-line digital holography

Abstract: In-line digital holography conciles the applicative interest of a simple optical set-up with the speed, low cost and potential of digital reconstruction. We address the twin-image problem that arises in holography due to the lack of phase information in intensity measurements. This problem is of great importance in in-line holography where spatial elimination of the twin-image cannot be carried out as in off-axis holography. Applications in digital holography of particle fields greatly depend on its suppression to reach greater particle concentrations, keeping a sufficient signal to noise ratio in reconstructed images. We describe in this paper methods to improve numerically the reconstructed images by twin-image reduction.

Acoustic Metamaterials for Noise Reduction: A Review

Abstract: Noise pollution has become a significant global problem in recent years. Unfortunately, conventional acoustic materials cannot offer substantial improvements in noise reduction. However, acoustic metamaterials are providing new solutions for controlling sound waves, and have huge potential for mitigating noise propagation in particular. Recently, owing to the rapid development of acoustic metamaterials, metamaterials for acoustic noise reduction have drawn the attention of researchers worldwide. These metamaterials are often both light and compact, and are excellent at reducing low‐frequency noise, which is difficult to control with conventional acoustic materials. Recent progress has illustrated that acoustic metamaterials effectively control sound waves, and optimizing their structure can enable functionality based on new physical phenomena. This review introduces the development of acoustic metamaterials, and summarizes the basic classification, underlying physical mechanism, application scenarios, and emerging research trends for both passive and active noise‐reduction metamaterials. Focusing on noise reduction, the shortcomings of current technologies are discussed, and future development trends are predicted. As our knowledge in this area continues to expand, it is expected that acoustic metamaterials will continue to improve and find more practical applications in emerging fields in the future.

Review of Underwater Acoustics Research: Noise

Abstract: The purposes, problems, and progress of radiated noise, self‐noise, and ambient noise research are reviewed. Purposes are related primarily to national defense, but applications to fishery and to the utilization of other natural resources are also noted. Basic problems, most of which were recognized 20 years or more ago, involve ascertainment of properties of the noise, identification of noise sources and mechanisms of noise generation, and the discovery and definition of noise dependencies on environmental factors. Many radiated and self‐noise sources and mechanisms have been identified. Major problems are those of noise measurement, noise reduction, and prevention. In the field of ambient noise, most measurements have been of sound‐pressure level. Some of the noise sources and environmental factors have been identified, and a capability for qualitative and gross prediction has been achieved. Recommended are further investigations of the variation of ambient noise with receiver depth, directionality of t...

The iDUDE Framework for Grayscale Image Denoising

Abstract: We present an extension of the discrete universal denoiser DUDE, specialized for the denoising of grayscale images. The original DUDE is a low-complexity algorithm aimed at recovering discrete sequences corrupted by discrete memoryless noise of known statistical characteristics. It is universal, in the sense of asymptotically achieving, without access to any information on the statistics of the clean sequence, the same performance as the best denoiser that does have access to such information. The DUDE, however, is not effective on grayscale images of practical size. The difficulty lies in the fact that one of the DUDE's key components is the determination of conditional empirical probability distributions of image samples, given the sample values in their neighborhood. When the alphabet is relatively large (as is the case with grayscale images), even for a small-sized neighborhood, the required distributions would be estimated from a large collection of sparse statistics, resulting in poor estimates that would not enable effective denoising. The present work enhances the basic DUDE scheme by incorporating statistical modeling tools that have proven successful in addressing similar issues in lossless image compression. Instantiations of the enhanced framework, which is referred to as iDUDE, are described for examples of additive and nonadditive noise. The resulting denoisers significantly surpass the state of the art in the case of salt and pepper (S&P) and -ary symmetric noise, and perform well for Gaussian noise.

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Abstract: Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network’s generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of $0.1dB$ , whether trained on or not.