Efficient Noise Reduction Method Research Articles

A machine-learning approach for noise reduction in parameter estimation inverse problems, applied to characterization of oil reservoirs

Reservoir characterization is an inverse problem where parameter values associated to the properties of the porous media, are estimated. In this problem, the pressure data and its log-derivative curves are used in the fitting process. However, the numerical differentiation needed to generate the log-derivative curve, is an ill-posed problem where the noise in the data can be largely propagated. This noise can produce several spurious local minima in the objective function and prevents to get a precise approximation to the parameters. Therefore, an efficient noise reduction method is required to achieve the desired accuracy in the parameter values.In this work, we explore three noise reduction methods by applying them to well-test data. These methods are based on multi-step finite differences and splines, but using a machine-learning approach.In addition, a new method is proposed which produce an optimal curve which is a linear combination of multiple data curves, that are approximations to the solution of the inverse problem.We show how these methods which use information from the forward model are more effcient for noise reduction. The proposed methods can be used in many inverse problems where there is a mathematical model meant to describe the measured data.

Random Noise Reduction with More Accurate Calibration for a Spectrometer Using a Two-Dimensional Array Detector

An efficient noise-reduction method with a more accurate wavelength calibration procedure is proposed for a spectrometer system to achieve a higher signal-to-noise ratio and spectral resolution. By using a two-dimensional (2D) array detector with an integrated grating composed of 10 sub-gratings, the system has a merit of high data acquisition speed to satisfy the condition in which in-situ spectral data analysis is critically required. The noise level has been effectively reduced down to approximately 25% of the previous level. The spectral curvature effect due to imperfection of the optical system is also analyzed and significantly reduced using the spectral calibration procedure to achieve higher resolution.

Image Noise Reduction Method Based on Compatibility with Adjacent Pixels

This paper proposes an efficient noise reduction method for gray and color images that are contaminated by salt-and-pepper noise. In the proposed method, the pixels that are more compatible with adjacent pixels are replaced with target (noisy) pixels. The algorithm is applied on noisy Lena and Mansion images that are contaminated by salt-and-pepper noise with 0.1 and 0.2 noise intensities. Although this method is developed for reducing noise from the images that are contaminated by salt-and-pepper noise, it can also reduce the noise from the images that are contaminated by other types of noises; yet it is more efficient for reducing salt-and-pepper noise. Both numerical and visual comparisons are demonstrated in the experimental simulations. The results show the proposed algorithm can successfully remove impulse noise from images that are contaminated by salt-and-pepper noise.

Parameterized Image Filtering Using fuzzy Logic

The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition. Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera, light levels and sensor temperature are major factors affecting the amount of blur in the resulting image. Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.

A Data-Driven Noise Reduction Method and Its Application for the Enhancement of Stress Wave Signals

Ensemble empirical mode decomposition (EEMD) has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs). The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP), are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE). The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.

Efficient impulse noise reduction via local directional gradients and fuzzy logic

This paper proposes an efficient noise reduction method for image restoration. A novel pixel classification method by using fuzzy logic and local gradients is first introduced to discriminate noise and noise-free pixels from corrupted images. Then, a switching filtering scheme is employed to noisy images. The noise pixels will be filtered by a weighted median filter with adaptive weights when the noise-free pixels are preserved in the filtered images. Both numerical comparisons and perceptual observations are demonstrated in the experimental simulations. It is found that the proposed method achieves a fairly better performance than a number of other existing methods in noise reduction and image restoration.