scholarly journals Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

2019 ◽  
Vol 10 (1) ◽  
pp. 243 ◽  
Author(s):  
Josep Arnal ◽  
Luis Súcar
Keyword(s):  
Noise Reduction ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Peer Group ◽  
Median Filter ◽  
Block Matching ◽  
Group Method ◽  
Hybrid Filter ◽  
Mixed Noise ◽  
Vector Median

To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).

scholarly journals New filtering technique for the impulsive noise reduction in color images

2004 ◽  
Vol 2004 (1) ◽  
pp. 79-91 ◽  
Author(s):  
B. Smolka ◽  
A. Chydzinski ◽  
K. N. Plataniotis ◽  
A. N. Venetsanopoulos
Keyword(s):  
Noise Reduction ◽  
Impulsive Noise ◽  
Median Filter ◽  
Computationally Efficient ◽  
Noise Component ◽  
Fuzzy Membership Functions ◽  
Novel Approach ◽  
Filtering Technique ◽  
Vector Median ◽  
Impulsive Noise Reduction

We present a novel approach to the problem of impulsive noise reduction for colorimages. The new image-filtering technique is based on the maximization of the similarities between pixels in the filtering window. Themethod is able to remove the noise component, while adapting itself to the local image structure. In this way, the proposed algorithm eliminates impulsive noise while preserving edges and fine image details. Since the algorithm can be considered as a modification of the vector median filter driven by fuzzy membership functions, it is fast, computationally efficient, and easy to implement. Experimental results indicate that the new method is superior, in terms of performance, to algorithms commonly used for impulsive noise reduction.

An Image Filtering Algorithm for Removing Mixed Noise

2013 ◽  
Vol 433-435 ◽  
pp. 383-388 ◽  
Author(s):  
Mao Xiang Chu ◽  
An Na Wang ◽  
Rong Fen Gong
Keyword(s):  
Gaussian Noise ◽  
Weighting Function ◽  
Median Filter ◽  
Bilateral Filter ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Simulation ◽  
Mixed Noise ◽  
Local Edge ◽  
Salt And Pepper

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

Infrared Image Denoising Based on Peer Groups with Median Filtering and Wiener Filtering

2013 ◽  
Vol 401-403 ◽  
pp. 1059-1062 ◽  
Author(s):  
Bao Shu Li ◽  
Ke Bin Cui ◽  
Xue Tao Xu ◽  
Wen Li Wei
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Peer Group ◽  
Median Filter ◽  
Infrared Image ◽  
Wiener Filtering ◽  
Median Filtering ◽  
Denoising Method ◽  
Sobel Operator ◽  
Boundary Information

With characteristics of impulse noise and Gaussian noise, we propose a new denoising method to infrared image. We use Sobel operator to obtain boundary information, and determine the denoising method based on the pixel number of the peer group, denoising impulse noise and Gaussian noise with median filter and Wiener filtering. Experimental results are provided to show that the proposed filter achieves a promising performance in PSNR and boundary information, compared with the median filtering, Wiener filtering and peer group algorithms.

scholarly journals Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

2018 ◽  
Vol 10 (10) ◽  
pp. 1600 ◽  
Author(s):  
Chang Li ◽  
Yu Liu ◽  
Juan Cheng ◽  
Rencheng Song ◽  
Hu Peng ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Bilinear Model ◽  
Hyperspectral Unmixing ◽  
Noise Impulse ◽  
Mixed Noise ◽  
Alternative Direction Method

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

A Combination Approach to Noise Reduction in Digital Image Processing

2013 ◽  
Vol 278-280 ◽  
pp. 1359-1365
Author(s):  
Jing Dong ◽  
Zhi Chai ◽  
Ke Wen Xia
Keyword(s):  
Noise Reduction ◽  
Digital Image ◽  
Gaussian Noise ◽  
Median Filter ◽  
The Other ◽  
Median Filtering ◽  
Mean Filter ◽  
The Mean ◽  
Combination Approach ◽  
Mean Filtering

In order to reduce Gaussian and Salt & Pepper noises, a combination approach to noise reduction is presented by combining the median filter with the mean filter. The detail simulations show that the mode which the median filtering first and then the mean filtering is superior to that of the simply single filtering, or the mean filtering first and then the median filtering when the image obviously contain the Salt & Pepper noise. On the other hand, it is not necessarily the optimal scheme to use the mode which the mean filtering first and then the median filtering when the digital image obviously contains the Gaussian noise.

Adaptive Edge Detection Method for Image Polluted Using Canny Operator and Otsu Threshold Selection

2011 ◽  
Vol 301-303 ◽  
pp. 797-804 ◽  
Author(s):  
Jian Guo Yang ◽  
Bei Zhi Li ◽  
Hua Jiang Chen
Keyword(s):  
Edge Detection ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Detection Method ◽  
Median Filter ◽  
Threshold Selection ◽  
Hybrid Filter ◽  
Canny Operator ◽  
Mean Filter ◽  
Edge Detection Method

In this paper, an adaptive edge detection method (Canny operator and Otsu threshold selection based adaptive edge detection method - COAED) is proposed. The COAED method combines a new hybrid filter with Canny operator to avoid the conflict of Canny operator between noise removing and edge locating, and uses Otsu threshold selection method to determine Dual-threshold of Canny operator adaptively. The new hybrid filter firstly judges whether the pixel is polluted by impulse noise, and then uses a corresponding filter to process the current pixel. A median filter is used if the pixel is thought to be impulse noise; otherwise an improved mean filter is selected to weaken the Gaussian noise. After the image is smoothed by the hybrid filter, a Canny operator with small Gaussian variance is used to extract edge. Because only part of Gaussian noise remains, Canny operator with small Gaussian variance can suppress the noise and preserve the edge effectively. Using the gauge image polluted by hybrid noise as experiment object, the performance of COAED method is evaluated qualitatively and quantitatively. Experimental results show that the COAED method is superior to Canny operator.

scholarly journals An Improved Image Filtering Algorithm for Mixed Noise

2021 ◽  
Vol 11 (21) ◽  
pp. 10358
Author(s):  
Chun He ◽  
Ke Guo ◽  
Huayue Chen
Keyword(s):  
Gaussian Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Block Matching ◽  
Threshold Function ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Mixed Noise ◽  
Salt And Pepper

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.

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