Improved Sparse 3D Transform-Domain Collaborative Filter for Screen Content Image Denoising

2017 ◽  
Vol 32 (03) ◽  
pp. 1854006 ◽  
Author(s):  
Zhi Liu ◽  
Shuai Wang ◽  
Mengmeng Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Transform Domain ◽  
Quality Improvements ◽  
Signal To Noise ◽  
Ratio Increase ◽  
Collaborative Filter ◽  
Block Classification ◽  
Screen Content ◽  
Screen Sharing

Screen content videos or images are widely used in applications such as screen sharing. Compressed screen content videos or images may have distortions or noises because of the quantization process. This paper proposes an improved sparse 3D transform-domain collaborative filter to enhance screen content image quality by block classification and block segmentation. Experimental results show that the proposed algorithm achieves a peak signal-to-noise ratio increase and subjective visual quality improvements for reconstructed screen content images.

scholarly journals Perona-Malik Model with Diffusion Coefficient Depending on Fractional Gradient via Caputo-Fabrizio Derivative

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Gustavo Asumu Mboro Nchama ◽  
Angela Leon Mecias ◽  
Mariano Rodriguez Ricard
Keyword(s):  
Image Processing ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Absolute Error ◽  
Natural Images ◽  
Mean Square ◽  
Signal To Noise ◽  
Major Drawback ◽  
Diffusivity Function

The Perona-Malik (PM) model is used successfully in image processing to eliminate noise while preserving edges; however, this model has a major drawback: it tends to make the image look blocky. This work proposes to modify the PM model by introducing the Caputo-Fabrizio fractional gradient inside the diffusivity function. Experiments with natural images show that our model can suppress efficiently the blocky effect. Also, our model has good performance in visual quality, high peak signal-to-noise ratio (PSNR), and lower value of mean absolute error (MAE) and mean square error (MSE).

scholarly journals Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Keya Huang ◽  
Hairong Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Visual Quality ◽  
Noise Removal ◽  
Interactive Systems ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Adaptive Median Filter ◽  
Mixed Noise ◽  
Filtering Effect

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.

A New De-Noising Arithmetic in the Output Signal of FOG

2009 ◽  
Vol 16-19 ◽  
pp. 273-277 ◽  
Author(s):  
Ying Liu ◽  
Yan Li ◽  
Jin Tao Xu
Keyword(s):  
Mathematical Model ◽  
Wavelet Transform ◽  
Output Signal ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Threshold Values ◽  
Signal To Noise ◽  
Noise Error ◽  
White Noises

To reduce the noise error existing in the output signal of fiber optic gyroscopes (FOGs) and increase the precision of the FOGs, this paper established a mathematical model of the FOGs output signal, analyzed the error characteristics of the FOGs output signal, put forward a new de-noising arithmetic based on the wavelet transform, soft and hard threshold compromise filtering, threshold values were determined by multi-dimensions recursion arithmetic. Through experiment, it has been already validated that the proposed approach had the competitive performances on visual quality, signal to noise ratio (SNR) and the standard variation, it is effective in eliminating the white noises existing in the output signal of the FOG.

scholarly journals Enhancing PIGPEN Image Steganography Method by Using Zigzag Scanning

2019 ◽  
pp. 1-10
Author(s):  
A. A. Abdelmgeid ◽  
A. A. Bahgat ◽  
Al-Hussien Seddik Saad ◽  
Maha Mohamed Gomaa
Keyword(s):  
Digital Images ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Image Steganography ◽  
High Peak ◽  
Secret Message ◽  
The Internet ◽  
Signal To Noise ◽  
Art And Science ◽  
File Formats

Steganography is the art and science of writing hidden messages in such a way that no one suspects the existence of the message, a form of security through obscurity. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the internet. In this paper explains the PIGPEN image steganography technique which modifies the secret message itself not the technique of embedding. This technique represents the secret message characters by two decimal digits only not three decimal digits as ASCII encoding. So, it can save one third of the required space for embedding the message in an image. The PIGPEN technique will be enhanced by using the zigzag scanning to increase the security and achieves higher visual quality as indicated by the high peak signal-to-noise ratio (PSNR) in spite of hiding a large number of secret bits in the image.

scholarly journals Denoising and Dehazing an Image in a Cascaded Pattern for Continuous Casting

Metals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 126
Author(s):  
Wenbin Su ◽  
Yifei Zhang ◽  
Hongbo Wei ◽  
Qi Gao
Keyword(s):  
Machine Learning ◽  
Continuous Casting ◽  
Steel Industry ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Validation Dataset ◽  
Signal To Noise ◽  
Complex Formulation

Automatic vision systems have been widely used in the continuous casting of the steel industry, which improve efficiency and reduce labor. At present, high temperatures with evaporating fog cause images to be noisy and hazy, impeding the usage of advanced machine learning algorithms in this task. Instead of considering denoising and dehazing separately like previous papers, we established that by taking advantage of deep learning in a modeling complex formulation, our proposed algorithm, called Cascaded Denoising and Dehazing Net (CDDNet) reduces noise and hazy in a cascading pattern. Experimental results on both synthesized images and a pragmatic video from a continuous casting factory demonstrate our method’s superior performance in various metrics. Compared with existing methods, CDDNet achieved a 50% improvement in terms of peak signal-to-noise ratio on the validation dataset, and a nearly 5% improvement on a dataset that has never seen before. Besides, our model generalizes so well that processing a video from an operating continuous casting factory with CDDNet resulted in high visual quality.

Quantum watermarking algorithm based on chaotic affine scrambling

2019 ◽  
Vol 17 (04) ◽  
pp. 1950038
Author(s):  
RiGui Zhou ◽  
YouDe Cheng ◽  
Hou Ian ◽  
XingAo Liu
Keyword(s):  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Extraction Process ◽  
Watermark Image ◽  
Signal To Noise ◽  
Quantum Watermarking ◽  
Embedding Strategy ◽  
Simulation Results ◽  
Color Value ◽  
Carrier Image

In order to improve the security of watermark image, a scheme of quantum watermarking algorithm which is based on chaotic affine scrambling is proposed and it includes scrambling, embedding and extracting procedures. In the embedding process, the position and the color of the watermark image are scrambled by chaotic affine and the size of the scrambled watermark image is extended from [Formula: see text] to [Formula: see text]. Meanwhile, the color value of the pixel is changed from 24-bits to 3-[Formula: see text](1-bit per channel) bits. The extended watermark image is embedded into the carrier image through a two-bit embedding strategy, and the extraction process is the inverse one of the embedding process. The simulation results show that the proposed scheme is superior to the comparison scheme in terms of visual quality, peak signal-to-noise ratio (PSNR).

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 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 Signal-to-noise ratio increase in carotid atheroma MRI: a comparison of 1.5 and 3 T

2012 ◽  
Vol 85 (1015) ◽  
pp. 937-944 ◽  
Author(s):  
V E Young ◽  
A J Patterson ◽  
E M Tunnicliffe ◽  
U Sadat ◽  
M J Graves ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Ratio Increase ◽  
Noise Ratio
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