Image denoising using spatial domain filters: A quantitative study

Sharma, Anmol; Sidhu, Jagroop Singh

doi:10.1109/cisp.2013.6744005

Cited by 21 publications

(12 citation statements)

References 11 publications

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“…Hence the ROI is resized using the above calculated scaling coefficient k. The ROIs are enhanced by removing noise in the spatial domain using a basic median filter [10].…”

Section: Databasementioning

confidence: 99%

Offline Handwritten Signature Verification using Zernike Moments

Kaur

Sharma

2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today remains to be handwritten signature. It is the most natural method of authenticating a person's identity as compared to other biometric and cryptographic forms of authentication. We propose a method for verifying the signatory's identity by using Zernike Moments as global shape descriptors. Zernike Moments are image moments that are rotation invariant. The moments are also orthogonal on a unit circle which ensures minimum redundancy between the features representing the object shape. The features extracted in our approach have a relatively low dimensionality as compared to other studies, while retaining high representation power of the moments. Moreover, the module developed using our approach was able to demonstrate high performance coupled with low computation times in testing phase, making it suitable for real time applications. Experiments show high overall performance of our approach with an equal error rate EER of 13.42% and area under the curve Az equal to 0.84 using 1564 images from the NFI SigComp2009 dataset.

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“…Hence the ROI is resized using the above calculated scaling coefficient k. The ROIs are enhanced by removing noise in the spatial domain using a basic median filter [10].…”

Section: Databasementioning

confidence: 99%

Offline Handwritten Signature Verification using Zernike Moments

Kaur

Sharma

2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

Self Cite

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“…As Far as Image denoising is accomplished by various methods: Time-domain, Frequency-domain, and Time-Frequency combination. Spatial domain methods do not account for the temporal correlation between the frames [1][2][3]. From an image Spatial noise is being successfully removed by Spatial filters resulting in achievement of high gain failed in restoring the edges particularly in less noisy areas [4].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Wavelet based Medical Videos using Hidden Markov Tree Model

Bangash¹,

Tauqir²,

Maqsood³

2021

KJCIS

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Wavelet based statistical image denoising is vital preprocessing technique in real world imaging. Most of the medical videos inherit system introduced noise during acquisition as a result of additional image capturing techniques, resulting in the poor quality of video for examination. So, we needed a trade-off between preservation and noise reduction of the actual image content to retains all the necessary information. The existing techniques are based on time-frequency domain where the wavelet coefficients need to be independent or jointly Gaussian. In denoising arena there is a need to exploit the temporal dependencies of wavelet coefficients with non-Gaussian nature. Here we present a YCbCr (Luminance-Chrominance) based denoising strategy on Hidden Markov Model (HMM) based on Multiresolution Analysis in the framework of Expectation-Maximization algorithm. Proposed algorithm applies denoising technique independently on each frame of the video. It models Non-Gaussian statistics of each wavelet coefficient and captures the statistical dependencies between coefficients. Denoised frames are restored inversely by processing the wavelet coefficients. Significant results are visualized through objective as well as subjective analysis. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) like parameters are used for the quality assessment of proposed method in comparison with Gray scale and Red, Green, Blue (RGB) scale videos coefficients.

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“…the past and the future frame). Spatial domain methods of denoising are limited in their scope as these methods do not take temporal correlation between frames into account [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Based Video Denoising using Probabilistic Models

Maqsood¹,

Touqir²,

Siddiqui³

et al. 2019

Mehran Univ. res. j. eng. technol.

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Wavelet based image processing techniques do not strictly follow the conventional probabilistic models that are unrealistic for real world images. However, the key features of joint probability distributions of wavelet coefficients are well captured by HMT (Hidden Markov Tree) model. This paper presents the HMT model based technique consisting of Wavelet based Multiresolution analysis to enhance the results in image processing applications such as compression, classification and denoising. The proposed technique is applied to colored video sequences by implementing the algorithm on each video frame independently. A 2D (Two Dimensional) DWT (Discrete Wavelet Transform) is used which is implemented on popular HMT model used in the framework of Expectation-Maximization algorithm. The proposed technique can properly exploit the temporal dependencies of wavelet coefficients and their non-Gaussian performance as opposed to existing wavelet based denoising techniques which consider the wavelet coefficients to be jointly Gaussian or independent. Denoised frames are obtained by processing the wavelet coefficients inversely. Comparison of proposed method with the existing techniques based on CPSNR (Coloured Peak Signal to Noise Ratio), PCC (Pearson’s Correlation Coefficient) and MSSIM (Mean Structural Similarity Index) has been carried out in detail.The proposed denoising method reveals improved results in terms of quantitative and qualitative analysis for both additive and multiplicative noise and retains nearly all the structural contents of a video frame.

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Image denoising using spatial domain filters: A quantitative study

Cited by 21 publications

References 11 publications

Offline Handwritten Signature Verification using Zernike Moments

Offline Handwritten Signature Verification using Zernike Moments

Two-Dimensional Wavelet based Medical Videos using Hidden Markov Tree Model

Wavelet Based Video Denoising using Probabilistic Models

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