scholarly journals Research on the Image Denoising Method Based on Partial Differential Equations

2016 ◽  
Vol 16 (5) ◽  
pp. 109-118
Author(s):  
Xiaolu Xie
Keyword(s):  
Image Denoising ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
New Approach ◽  
Proposed Model ◽  
Index Measure

Abstract In this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.

scholarly journals A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

2020 ◽  
Vol 12 (14) ◽  
pp. 2336 ◽  
Author(s):  
Shaobo Li ◽  
Jianhu Zhao ◽  
Hongmei Zhang ◽  
Zijun Bi ◽  
Siheng Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Low Rank ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Non Local ◽  
Better Than ◽  
Total Average

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

scholarly journals Image Denoising by using Modified SGHP Algorithm

2018 ◽  
Vol 8 (2) ◽  
pp. 971
Author(s):  
Sreedhar Kollem ◽  
K. Ramalinga Reddy ◽  
D. Sreenivasa Rao
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Natural Images ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Noisy Observation ◽  
Noise Ratio

In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.

Denoising and 3D Reconstruction of CT Images in Extracted Tooth via Wavelet and Bilateral Filtering

2018 ◽  
Vol 32 (05) ◽  
pp. 1854010 ◽  
Author(s):  
Cuizhen Wang ◽  
Zhenxue Chen ◽  
Yan Wang ◽  
Zhifeng Wang
Keyword(s):  
3D Reconstruction ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Bilateral Filtering ◽  
Denoising Method ◽  
Dimensional Reconstruction ◽  
Index Measure

Three-dimensional reconstruction of teeth plays an important role in the operation of living dental implants. However, the tissue around teeth and the noise generated in the process of image acquisition bring a serious impact on the reconstruction results, which must be reduced or eliminated. Combined with the advantages of wavelet transform and bilateral filtering, this paper proposes an image denoising method based on the above methods. The method proposed in this paper not only removes the noise but also preserves the image edge details. The noise in high frequency subbands is denoised using a locally adaptive thresholding and the noise in low frequency subbands is filtered by the bilateral filtering. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and 3D reconstruction using the iso-surface extraction method are used to evaluate the denoising effect. The experimental results show that the proposed method is better than the wavelet denoising and bilateral filtering, and the reconstruction results meet the requirements of clinical diagnosis.

scholarly journals Multi-wavelet level comparison on compressive sensing for MRI image reconstruction

2020 ◽  
Vol 9 (4) ◽  
pp. 1461-1467
Author(s):  
Indrarini Dyah Irawati ◽  
Sugondo Hadiyoso ◽  
Yuli Sun Hariyani
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Gaussian Random Process ◽  
Structural Similarity Index ◽  
Level 3 ◽  
Mri Reconstruction ◽  
Multi Level ◽  
Index Measure ◽  
Level 2

In this study, we proposed compressive sampling for MRI reconstruction based on sparse representation using multi-wavelet transformation. Comparing the performance of wavelet decomposition level, which are Level 1, Level 2, Level 3, and Level 4. We used gaussian random process to generate measurement matrix. The algorithm used to reconstruct the image is . The experimental results showed that the use of wavelet multi-level can generate higher compression ratio but requires a longer processing time. MRI reconstruction results based on the parameters of the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) show that the higher the level of decomposition in wavelets, the value of both decreases.

scholarly journals Digital Image Steganography Scheme Based on DWT and SVD

2020 ◽  
Vol 13 (4) ◽  
pp. 10-17
Author(s):  
Fadhil Kadhim Zaidan
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Grayscale Image ◽  
Structural Similarity Index ◽  
Value Decomposition ◽  
Index Measure

In this work, a grayscale image steganography scheme is proposed using a discrete wavelet transform (DWT) and singular value decomposition (SVD). In this scheme, 2-level DWT is applied to a cover image to obtain the high frequency band HL2 which is utilized to embed a secret grayscale image based on the SVD technique. The robustness and the imperceptibility of the proposed steganography algorithm are controlled by a scaling factor for obtaining an acceptable trade-off between them. Peak signal to noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used for assessing the efficiency of the proposed approach. Experimental results demonstrate that the proposed scheme still holds its validity under different known attacks such as noise addition, filtering, cropping and JPEG compression

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 A Novel Approach for Image Denoising and Performance Analysis using SGO and APSO

2021 ◽  
Vol 2070 (1) ◽  
pp. 012139
Author(s):  
V MNSSVKR Gupta ◽  
KVSS Murthy ◽  
R Shiva Shankar
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Threshold Function ◽  
Structural Similarity Index ◽  
Novel Approach ◽  
Accelerated Particle Swarm Optimization ◽  
And Performance ◽  
Social Group Optimization

Abstract Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).

scholarly journals Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

2020 ◽  
Vol 37 (6) ◽  
pp. 955-964
Author(s):  
Nadhir Nouioua ◽  
Ali Seddiki ◽  
Abdelkrim Ghaz
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Cover Image ◽  
Healthcare Sector ◽  
Complex Wavelet Transform ◽  
Structural Similarity Index ◽  
Noise Image ◽  
Core Idea ◽  
Index Measure

In this work, a blind watermarking framework for medical images based on Dual-Tree Complex Wavelet Transform (DTCWT) and Non-subsampled Contourlet Transform (NSCT) is proposed. The core idea of this technique is to embed the watermark in the appropriate NSCT sub-band obtained by decomposing the cover image low coefficients purchased from the DTCWT standing on a quantization embedding function, the extraction phase is done without the requirement of the original cover image, what makes it a fully blind process. As clarity and integrity of the retrieved watermark are mandatory, a series of tests were exerted to affirm the robustness of the proposed scheme. The effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack. Furthermore, the method in hands outperformance within the quantitative comparisons with other techniques in the literature in terms of rapid execution time, and quality extraction of hidden information, and appropriateness to be integrated for secure exchange in the healthcare sector.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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