Image De-noising by Various Filters for Different Noise

Patidar, Pawan; Gupta, Manoj; Srivastava, Sumit; Nagawat, Ashok K.

doi:10.5120/1370-1846

Cited by 146 publications

(117 citation statements)

References 4 publications

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“…To reduce the noise level in the radar backscatter, a low pass Wiener filter (3 × 3 cell, the smallest possible to maintain a high-resolution) is applied to the images. Adaptive Wiener filters perform better than conventional mean or median filters in removing typical SAR speckle noise [37,38].…”

Section: Pre-processing Sar Imagesmentioning

confidence: 99%

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

et al. 2017

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This paper presents a semi-automated approach to detecting coastal shoreline change with high spatial-and temporal-resolution using X-band synthetic aperture radar (SAR) data. The method was applied at the Sand Motor, a "mega-scale" beach nourishment project in the Netherlands. Natural processes, like waves, wind, and tides, gradually distribute the highly concentrated sand to adjacent beaches. Currently, various in-situ techniques are used to monitor the Sand Motor on a monthly basis. Meanwhile, the TerraSAR-X satellite collects two high-resolution (3 × 3 m), cloud-penetrating SAR images every 11 days. This study investigates whether shorelines detected in TerraSAR-X imagery are accurate enough to monitor the shoreline dynamics of a project like the Sand Motor. The study proposes and implements a semi-automated workflow to extract shorelines from all 182 available TerraSAR-X images acquired between 2011 and 2014. The shorelines are validated using bi-monthly RTK-GPS topographic surveys and nearby wave and tide measurements. A valid shoreline could be extracted from 54% of the images. The horizontal accuracy of these shorelines is approximately 50 m, which is sufficient to assess the larger scale shoreline dynamics of the Sand Motor. The accuracy is affected strongly by sea state and partly by acquisition geometry. We conclude that using frequent, high-resolution TerraSAR-X imagery is a valid option for assessing coastal dynamics on the order of tens of meters at approximately monthly intervals.

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Section: Pre-processing Sar Imagesmentioning

confidence: 99%

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

et al. 2017

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“…An adaptive filter iteratively adjusts its parameters during scanning the image to match the image generating mechanism. Adaptive Filters reduces speckle noise in homogeneous areas, preserves the texture and high frequency information in heterogeneous areas [12].Adaptive filters take into account local image statistics while carrying out the filtration process, and hence they are able to remove the noise while preserving the texture. The different types of adaptive filters are lee Filter [3] [10],Modified Lee [13], Modified Frost filters [13] and SRAD(Speckle reducing anisotropic diffusion) [14] Filter.…”

Section: Adaptive Filtersmentioning

confidence: 99%

“…This includes deblurring of images degraded by the limitations of a sensor or its environment, noise filtering and correction of geometric distortion or non-linearties due to sensors fundamental result in filtering theory. Generally speaking there are two techniques of removing/reducing speckle noise [1], i.e., multi-look process and spatial filtering [12]. Multi-look process is used at the data acquisition stage 2 The Existing Filtering Methods…”

Section: Introductionmentioning

confidence: 99%

Denoising the Underwater Images by using Adaptive Filters

Jeelani¹,

Veena²

2017

AIVP

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The Sound Navigation And Ranging and synthetic aperture radar images are perturbed by the multiplicative noise called speckle noise . The presence of speckle noise leads to incorrect analysis and has to be handled carefully. Images have a strong variation from one pixel to another which reduces the efficiency of the algorithms for detection and classification. In this paper, the most well-known filters are analyzed by using underwater images. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Linear noise removing models can remove noise but are not able to preserve edges of the images in an efficient manner . On the other hand, Non-linear models can handle edges in a much better way than linear models. It is found that the linear filters and nonlinear filters can remove noise from small area objects and homogeneous areas but not in heterogeneous areas. Adaptive filters are used to remove noise not only from homogeneous area but also from heterogeneous areas. This paper presents an optimum filter by using locally estimated parameter values to provide minimum mean square error in order to smoothen the sonar images. It is also shown that the optimum filter is computationally efficient. The performance of this adaptive filter is compared (qualitatively) with other filters and found that the complexity of the frost filter is reduced.

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“…The Haar wavelet packet transform is usually referred to as the Walsh transform. [2]The Discrete Fourier Transform (DFT) may be thought of in general terms as a matrix multiplication in which the original vector In the above we may derive the transformed coefficients n X by inverting the matrix. The form of kn W has many possibilities but physically we would like the option of forward and backward transforms ie., an inverse ought to exist.…”

Section: Introduction To Wavelet Packet Transformmentioning

confidence: 99%

Wavelet & Other Thresholding Technique in Digital Image Processing

2017

IJSR

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Abstract:The past decade has witnessed the development of wavelet analysis, a new tool that emerged from mathematics and was quickly adopted by diverse fields of science and engineering. In the brief period since its creation in 1987-88, it has reached a certain level of maturity as a well-defined mathematical discipline, with its own conferences, journals, research monographs, and textbooks proliferating at a rapid rate. Wavelet analysis has begun to play a serious role in a broad range of applications, including signal processing, data and image compression, solution of partial differential equations, modelling multiscale phenomena, and statistics. There seem to be no limits to the subjects where it may have utility.Our aim is to explore some additional topics that extend the basic ideas of wavelet analysis.

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Image De-noising by Various Filters for Different Noise

Cited by 146 publications

References 4 publications

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

Denoising the Underwater Images by using Adaptive Filters

Wavelet & Other Thresholding Technique in Digital Image Processing

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