Sign Summation Image and Its Application to Robust Image Registration
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This paper proposes sign summation (SS) images for robust image registration against noises. An SS image is generated as the summation of the signs of the differences between the focused pixel and some of its neighboring ones. For image registration in subpixel accuracy, phase-only correlation or cross-correlation is applied to SS images rather than nonprocessed images. Experimental rusults show that SS imaging improves estimation accuracy, whereas median filter, a well-known impulse noise suppression filter, degrades the accuracy.Keywords:
Subpixel rendering
Image registration
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In this paper we have introduced a new method for the enhancement of gray scale images, when images are corrupted by fixed valued impulse noise (salt and pepper noise). Our proposed method gives a better output for low-density impulse noise as compare to the other famous filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF) and Modified Decision Based Median Filter (MDBMF) and so on. In our proposed method we have improved the Image Enhancement factor (IEF), Peak signal to noise ratio (PSNR), visual perception and also reduce blurring in the image. The proposed algorithm replaces the noisy pixel by trimmed mean value. When previous pixel values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remain noisy pixels are replaced by mean value. Different gray-scale images are tested via proposed method. The experimental result shows better Peak Signal to Noise Ratio (PSNR) value, Image Enhancement Factor (IEF) and with better visual and human perception.
Salt-and-pepper noise
Impulse noise
Peak signal-to-noise ratio
Image noise
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Zhang & Karim's Laplacian-preprocessed detector (2002) is robust against mis-identification of an image's thin-lines as impulse-noise-corrupted pixels. Wang & Zhang's "long-range correlation" denoising scheme (January 1998) exploits any information-redundancy between an identified corrupted-pixel's local neighborhood with distant sub-images, to restore the corrupted pixel. This paper synergizes the above two algorithms, with the following algorithmic enhancements: (1) a pre-tuning of Zhang & Karim's threshold based on a rough estimation of the corrupting impulse-noise's spatial probability of occurrence, assuming the availability of a test-image "sufficiently" similar to the given corrupted image; and (2) a new "difference-mean" criterion for better pixel-restoration. Limited simulations illustrate the above proposed scheme's efficacy and improvements.
Impulse noise
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Noise degrades the image quality at a large extent in all kinds of images. Impulse noise is the most common one. Correlation is used for analysis of features of the filtering mask. In this work a new algorithm for de-noising, based on median filter is proposed. It has been implemented for the denoising of grayscale and color images which are corrupted by impulse noise. This median filter based algorithm removes the noisy pixels by median value or with the help of collective study of the processed neighboring pixel values. The problem of increasing window size is also removed. To check the efficiency and the performance of the proposed algorithm it has been compared with various filters and corresponding algorithms. The experiments are performed over a range of noise density, which ranges from 5% to 80% for Lena and Peppers images. Results are compared in terms of PSNR and MSE.
Impulse noise
Salt-and-pepper noise
Peak signal-to-noise ratio
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Impulse noise
Salt-and-pepper noise
Image noise
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An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.
Non-Local Means
Step detection
Peak signal-to-noise ratio
Video denoising
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Removed.
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Digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences.
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