A Novel Technique for Removal of High Density White Spot Noise from Digital Neutron Radiographic Images

This paper proposes a novel technique of adaptive switching alternative median (ASAM) filter for high-density white spot noise removal. The ASAM filter is composed of two blocks filtering, namely main and secondary block filtering, respectively. The proposed secondary block filtering is a new technique in high-density impulse noise removal and the main contribution of this research. The ASAM algorithm was tested on the standard 8-bit gray-scale, 512×512 pixel Lena image and a real neutron radiographic image. The results showed significant reduction of white spot noise in both types of images through visual inspection.    To measure the performance of noise removal in simulation test we measured the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, and denoising time, and in real application tests we measured signal-to-noise ratio (SNR). From the experiments of simulation test, at the highest level noise of 95 % the obtained PSNR and SSIM are 23.584 dB and 0.696 respectively. These are higher than the results of other algorithms that are 16.697 dB and 0.475, respectively, for DBA, 16.696 dB and 0.408 for NAFSM, and 18.860 dB and 0.568 for NASNLM. The denoising times for DBA, NAFSM, NASNLM, and ASAM were obtained as 6.469 s, 5.186 s, 36.735 s, and 5.197 s respectively. From the experiments of real application test we obtained the SNR for DBA, NAFSM, NASNLM, and ASAM as 32.42 dB, 6.01 dB, 18.77 dB, and 32.96 dB, respectively. In general, these results show that ASAM filter is superior to the existing filtering methods. The ASAM filter improved the image restoration quality, especially in removing the high-density white spot noise, and was able to yield good filtering result which exhibits better PSNR, SSIM, denoising time, and qualitative visual inspection

Video enhancement using adaptive spatio-temporal connective filter and piecewise mapping

This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises - Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. © 2004 IEEE.In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the sec ond filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (NSF) under Award EEC-9986821, by an ARO MURI on Demining under Grant DAAG55-97-1-0013, and by the NSF under Award 0208548