Removal Of Random Impulse Noise Research Articles

Quaternion Nuclear Norm Minus Frobenius Norm Minimization for color image reconstruction

Color image restoration methods typically represent images as vectors in Euclidean space or combinations of three monochrome channels. However, they often overlook the correlation between these channels, leading to color distortion and artifacts in the reconstructed image. To address this, we present Quaternion Nuclear Norm Minus Frobenius Norm Minimization (QNMF), a novel approach for color image reconstruction. QNMF utilizes quaternion algebra to capture the relationships among RGB channels comprehensively. By employing a regularization technique that involves nuclear norm minus Frobenius norm, QNMF approximates the underlying low-rank structure of quaternion-encoded color images. Theoretical proofs are provided to ensure the method’s mathematical integrity. Demonstrating versatility and efficacy, the QNMF regularizer excels in various color low-level vision tasks, including denoising, deblurring, inpainting, and random impulse noise removal, achieving state-of-the-art results.

Adaptive Threshold and Directional Weighted Median Filter-Based Impulse Noise Removal Method for Images

Elimination of impulse noise in image snap shots with side renovation is one of the complex duties in digital image processing. In this paper, the removal of random impulse noise is done in two important levels. In first level, the detection of the impulse noise is done on the premise of a double threshold selecting strategy after which in the another level, elimination of impulse noise is done by the usage of median filter and directional weighted median filter relying upon the noise map (Nmap) construction of corrupted pixels detected within the first level. The proposed method makes use of the statistical characteristics of noisy image graphs and the brink obtained is adaptable to one of a kind of snap shots and noise conditions. Comparative evaluation with different widespread de-noising techniques shows that the proposed method outperforms in terms of PSNR, SSIM, NMSE and Computation Time (CT) of the distinct trying out test images, with exclusive noise levels.

Directional 3D Peer-Group Filter for Color Image with Random Impulse Noise

Peer-group filtering is the most basic approach to image denoising in the spatial domain; however, the effectiveness of this method degrades rapidly with an increase in the amount of noise. In this paper, we propose a novel 3D (3 dimensions) directional peer-group filter (referred to as 3DPGF) for the restoration of color images through the removal of random impulse noise. As with other switching methods, 3DPGF proceeds through two steps. Noise detection stage is first performed using a directional peer-group method, whereupon a 3D peer-group weighted-mean technique is used to remove the noise. Simulation results demonstrate that 3DPGF is able to enhance the accuracy in the identification and removal of noise.

Quaternion-Based Impulse Noise Removal From Color Video Sequences

In this paper, a new quaternion vector filter for removal of random impulse noise in color video sequences is presented. First, luminance distances and chromaticity differences that are represented in quaternion form are combined together to measure color distances between color pixels. Then, based on this new color distance mechanism, the samples along horizontal, vertical, and diagonal directions in current frame and the samples of adjacent frames on motion trajectory are used to detect whether each pixel is noisy or not. By analyzing the spatiotemporal order-statistic information about these directional samples, the video pixels are classified into noise free and noisy. Finally, 3-D weighted vector median filtering is performed on the pixels that are judged as noisy, and the other pixels remain unchanged. The experimental results show that the proposed algorithm significantly outperforms other state-of-the-art video denoising methods in terms of both objective measure and visual evaluation.

A fuzzy filter for the removal of random impulse noise in image sequences

In this paper a new fuzzy filter for the removal of random impulse noise in digital grayscale image sequences is presented. The filter consists of different noise detection and filtering steps, in which the fuzzy set theory is used. This noise detection is based both on spatial and on temporal information and has the aim to prevent the filtering of noise free image pixels. The filtering of the detected noisy pixels is finally performed in a motion compensated way. Experimental results show that our method outperforms other state-of-the-art filters in terms of the peak-signal-to-noise ratio as well as visual quality.

Random impulse noise removal from image sequences based on fuzzy logic

In this paper a new filter for image sequences corrupted with random impulse noise is presented. In order to preserve the image details as much as possible, the noise is removed in different successive filtering steps. In each step, only the pixels that are detected as being noisy are filtered, while the noise-free pixels remain unchanged. The noise detection is based on fuzzy set theory and fuzzy rules, which are very useful for the processing of human knowledge and linguistic values. To exploit the temporal information in image sequences as much as possible, detected pixels are finally filtered in a motion compensated way. From the experimental results it can be seen that the proposed method outperforms other state-of-the-art filters both in terms of the peak-signal-to-noise ratio, the mean absolute error, and visually.

Fuzzy Random Impulse Noise Removal From Color Image Sequences

In this paper, a new fuzzy filter for the removal of random impulse noise in color video is presented. By working with different successive filtering steps, a very good tradeoff between detail preservation and noise removal is obtained. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. Pixels that are detected as noisy are filtered, the others remain unchanged. Filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).