scholarly journals Optimized Hybrid Model for Gaussian Noise Reduction images

2021 ◽  
Vol 19 (1) ◽  
pp. 9-17
Author(s):  
Lubna Farhi ◽  
Agha Yasir ◽  
Farhan Ur Rehman ◽  
Baqar A. Zardari ◽  
Ramsha Shakeel
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.

scholarly journals Hybrid FWF Model For Gaussian Noise Reduction From Images

10.31645/11 ◽  
2020 ◽  
Author(s):  
Lubna Farhi ◽  
◽  
Farhan Ur Rehman ◽  
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, the image efficiency is improved by using hybrid model of wiener’s filter and fuzzy filter. It’s a challenging task to remove Gaussian noise (GN) from an image and to protect the picture edges. The Fuzzy - Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results presented that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques

scholarly journals Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanqiu Zeng ◽  
Baocan Zhang ◽  
Wei Zhao ◽  
Shixiao Xiao ◽  
Guokai Zhang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Electronic Noise ◽  
Mr Images ◽  
Electronic Components ◽  
Mr Image ◽  
Residual Part ◽  
Soft Threshold

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

scholarly journals Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks

2019 ◽  
Vol 9 (5) ◽  
pp. 1009 ◽  
Author(s):  
Hui Fan ◽  
Meng Han ◽  
Jinjiang Li
Keyword(s):  
Signal To Noise Ratio ◽  
Network Models ◽  
Structural Similarity ◽  
Shadow Removal ◽  
Deep Convolutional Neural Networks ◽  
Edge Information ◽  
Qualitative And Quantitative ◽  
Automatic Methods ◽  
End To End ◽  
High Level

Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder–decoder network and a small refinement network. The former predicts the alpha shadow scale factor, and the latter refines to obtain sharper edge information. In addition, a new image database (remove shadow database, RSDB) is constructed; and qualitative and quantitative evaluations are made on databases such as UIUC, UCF and newly-created databases (RSDB) with various real images. Using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) for quantitative analysis, the algorithm has a big improvement on the PSNR and the SSIM as opposed to other methods. In terms of qualitative comparisons, the network shadow has a clearer and shadow-free image that is consistent with the original image color and texture, and the detail processing effect is much better. The experimental results show that the proposed algorithm is superior to other algorithms, and it is more robust in subjective vision and objective quantization.

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

scholarly journals HLS Based Approach to Develop an Implementable HDR Algorithm

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 332 ◽  
Author(s):  
Rappy Saha ◽  
Partha Banik ◽  
Ki-Doo Kim
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Simple Algorithm ◽  
Structural Similarity ◽  
High Dynamic Range ◽  
Field Programmable ◽  
Hardware Description ◽  
On Chip ◽  
High Level ◽  
Removal Technique

Hardware suitability of an algorithm can only be verified when the algorithm is actually implemented in the hardware. By hardware, we indicate system on chip (SoC) where both processor and field-programmable gate array (FPGA) are available. Our goal is to develop a simple algorithm that can be implemented on hardware where high-level synthesis (HLS) will reduce the tiresome work of manual hardware description language (HDL) optimization. We propose an algorithm to achieve high dynamic range (HDR) image from a single low dynamic range (LDR) image. We use highlight removal technique for this purpose. Our target is to develop parameter free simple algorithm that can be easily implemented on hardware. For this purpose, we use statistical information of the image. While software development is verified with state of the art, the HLS approach confirms that the proposed algorithm is implementable to hardware. The performance of the algorithm is measured using four no-reference metrics. According to the measurement of the structural similarity (SSIM) index metric and peak signal-to-noise ratio (PSNR), hardware simulated output is at least 98.87 percent and 39.90 dB similar to the software simulated output. Our approach is novel and effective in the development of hardware implementable HDR algorithm from a single LDR image using the HLS tool.

scholarly journals Echocardiography image denoising using fractal wavelet transform

2018 ◽  
Vol 5 ◽  
pp. 23-33
Author(s):  
Reena Manandhar ◽  
Sanjeeb Prashad Pandey
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Hard Thresholding ◽  
Echocardiography Image ◽  
Different Types

One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.

scholarly journals Overview of image noise reduction based on non-local mean algorithm

2018 ◽  
Vol 232 ◽  
pp. 03029 ◽  
Author(s):  
Baozhong LIU ◽  
Jianbin LIU
Keyword(s):  
Noise Reduction ◽  
Development Process ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Mean Square ◽  
Signal To Noise ◽  
Image Noise Reduction ◽  
Local Mean ◽  
Non Local ◽  
Noise Reduction Algorithm

The system introduces the extensive application and development process of image denoising based on non-local mean. The principle and specific theoretical model of the non-local mean algorithm are described. The process of improving the non-local mean algorithm after being proposed and how to improve it is elaborated and the shortcomings of the algorithm are pointed out. The noise reduction algorithm is experimentally described in detail from the aspects of peak signal-to-noise ratio, mean square error and structural similarity under different noise environment conditions.

scholarly journals Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter

2021 ◽  
Vol 36 (1) ◽  
pp. 642-649
Author(s):  
G. Sharvani Reddy ◽  
R. Nanmaran ◽  
Gokul Paramasivam
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Degraded Image ◽  
Restoration Algorithms ◽  
Index Measure

Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).

scholarly journals Comparative analysis of different image denoising methods in spatial and transform domain

2021 ◽  
Author(s):  
Zeeshan Ahmad
Keyword(s):  
Comparative Analysis ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Structural Similarity ◽  
Noise Model ◽  
Transform Domain ◽  
Noise Levels ◽  
Extract Information

Digital Images are the best source for humans to see, visualize, think, extract information and make conclusions. However during the acquisition of images, noise superimposes on the images and reduces the information and detail of the images. In order to restore the details of the images, noise must be reduced from the images. This requirement places the image denoising amongst the fundamental and challenging fields of computer vision and image processing. In this project six fundamental techniques / algorithms of image denoising in spatial and transform domain are presented and their comparative analysis is also carried out. The noise model used in this project is Additive Gaussian noise. The algorithms are simulated on Matlab and experimental results are shown at different noise levels. The performance of each image denoising technique is measured in terms of Peak Signal to Noise Ratio (PSNR) , Mean Structural Similarity (SSIM) Metrics and visual quality. It is observed that the transform domain techniques used in this project achieved better results as compared to spatial domain techniques

scholarly journals Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 265-268
Author(s):  
G.Manmadha Rao* ◽  
Raidu Babu D.N ◽  
Krishna Kanth P.S.L ◽  
Vinay B. ◽  
Nikhil V.
Keyword(s):  
Impulse Noise ◽  
Impulsive Noise ◽  
Rank Order ◽  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Window Size ◽  
Mean Value ◽  
Audio Signals ◽  
Signal To Noise ◽  
Noise Ratio

Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.

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