scholarly journals Hyperspectral Image Restoration under Complex Multi-Band Noises

2018 ◽  
Vol 10 (10) ◽  
pp. 1631 ◽  
Author(s):  
Zongsheng Yue ◽  
Deyu Meng ◽  
Yongqing Sun ◽  
Qian Zhao
Keyword(s):  
Dirichlet Process ◽  
Hyperspectral Image ◽  
Similarity Index ◽  
Distinct Type ◽  
Structural Similarity ◽  
Gaussian Mixture ◽  
Noise Characteristics ◽  
Restoration Technique ◽  
The Mean ◽  
Bayes Algorithm

Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-level hierarchical Dirichlet process (HDP) to model the HSI noise structure, the noise of each band is depicted by a Dirichlet process Gaussian mixture model (DP-GMM), in which its complexity can be flexibly adapted in an automatic manner. Besides, the DP-GMM of each band comes from a higher level DP-GMM that relates the noise of different bands. The variational Bayes algorithm is also designed to solve this model, and closed-form updating equations for all involved parameters are deduced. The experiment indicates that, in terms of the mean peak signal-to-noise ratio (MPSNR), the proposed method is on average 1 dB higher compared with the existing state-of-the-art methods, as well as performing better in terms of the mean structural similarity index (MSSIM) and Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS).

scholarly journals A robust watermarking algorithm for medical images

2020 ◽  
Vol 20 (3) ◽  
pp. 1601
Author(s):  
Ahmed Nagm ◽  
Mohammed Safy
Keyword(s):  
Medical Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Patient Privacy ◽  
Secret Code ◽  
The Mean ◽  
Image Quality Index

<p>Integrated healthcare systems require the transmission of medical images between medical centres. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be considered while watermarking an image. Among these issues, the watermark should be robust against attacks and does not affect the quality of the image. In this paper, a watermarking approach employing a robust dynamic secret code is proposed. This approach is to process every pixel of the digital image and not only the pixels of the regions of non-interest at the same time it preserves the image details. The performance of the proposed approach is evaluated using several performance measures such as the Mean Square Error (MSE), the Mean Absolute Error (MAE), the Peak Signal to Noise Ratio (PSNR), the Universal Image Quality Index (UIQI) and the Structural Similarity Index (SSIM). The proposed approach has been tested and shown robustness in detecting the intentional attacks that change image, specifically the most important diagnostic information.</p>

scholarly journals The digital reconstruction of degraded ancient temple murals using dynamic mask generation and an extended exemplar-based region-filling algorithm

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
V. Rakhi Mol ◽  
P. Uma Maheswari
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Human Beings ◽  
Reconstruction Procedure ◽  
Pixel Intensity ◽  
Digital Reconstruction ◽  
A Value ◽  
Restoration Technique ◽  
Region Filling

AbstractA mural is any piece of artwork sculpted or applied directly on a wall, ceiling or other permanent surface. This artwork symbolizes various culture’s, traditions, historical events, spiritual stories, and civilizations of respective societies of ancient times. But these mural paintings are subjected to degradation either by various natural causes as well as pollution or by human beings without knowing their value. Restoring these paintings requires skilled artisans who are hard to find these days. Consequently, an efficient image restoration technique is required to meet the particular needs of the paintings. Existing in-painting algorithms largely use pixel-based textural reconstruction. The technique, however, does not work well for images with large, degraded portions. and also fails in the restoration of the structure. To resolve these drawbacks, we propose a combined technique for the textural and structural reconstruction of ancient murals. The proposed Extended Exemplar-based Region-Filling Algorithm uses a patch-based reconstruction procedure and masked images are created automatically using the Dynamic Mask Generation Algorithm. The deteriorated portions are identified by creating masks, and masks are created in such a way that degraded portions have a pixel intensity value of one and the remaining part has a value of zero, and filling is done by analyzing the surrounding pixel values of the degraded pixel. The algorithm reconstructs the structure of the paintings efficiently by generating sketches. The proposed technique reconstructs both the structure and textural information, and ensures efficient reconstructed results, compared to existing in-painting techniques. Performance is evaluated by metrics such as the Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM).

scholarly journals Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation

2018 ◽  
Vol 10 (12) ◽  
pp. 1956 ◽  
Author(s):  
Le Sun ◽  
Tianming Zhan ◽  
Zebin Wu ◽  
Liang Xiao ◽  
Byeungwoo Jeon
Keyword(s):  
Total Variation ◽  
Hyperspectral Image ◽  
Similarity Index ◽  
Specific Pattern ◽  
Low Rank ◽  
Dimensional Manifold ◽  
Spectral Difference ◽  
Low Rank Approximation ◽  
Rank Approximation ◽  
The Mean

Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method is proposed for hyperspectral mixed denoising. Spectral difference transform, which projects data into spectral difference space (SDS), has been proven to be powerful at changing the structures of noises (especially for sparse noise with a specific pattern, e.g., stripes or dead lines present at the same position in a series of bands) in an original hyperspectral image (HSI), thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. In addition, because the neighboring pixels are highly correlated and the spectra of homogeneous objects in a hyperspectral scene are always in the same low-dimensional manifold, we are inspired to combine total variation and the nuclear norm to simultaneously exploit the local piecewise smoothness and global low rankness in SDS for mixed noise reduction of HSI. Finally, the alternating direction methods of multipliers (ADMM) is employed to effectively solve the SDTVLA model. Extensive experiments on three simulated and two real HSI datasets demonstrate that, in terms of quantitative metrics (i.e., the mean peak signal-to-noise ratio (MPSNR), the mean structural similarity index (MSSIM) and the mean spectral angle (MSA)), the proposed SDTVLA method is, on average, 1.5 dB higher MPSNR values than the competitive methods as well as performing better in terms of visual effect.

scholarly journals Clustering-Based Band Selection Using Structural Similarity Index and Entropy for Hyperspectral Image Classification

2020 ◽  
Vol 37 (5) ◽  
pp. 785-791
Author(s):  
Arsalan Ghorbanian ◽  
Yasser Maghsoudi ◽  
Ali Mohammadzadeh
Keyword(s):  
Image Classification ◽  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Similarity Index ◽  
Structural Similarity ◽  
Kappa Coefficient ◽  
Hyperspectral Image Classification ◽  
Structural Similarity Index ◽  
Classification Tasks

Despite the unique capabilities of hyperspectral images for classification tasks, handling the high dimension of these data is challenging. Therefore, dimension reduction algorithms have been proposed to solve this challenge. In this paper, an unsupervised Feature Selection (FS) algorithm was proposed for hyperspectral image classification. First, the entropy values of hyperspectral bands were employed to identify and remove noisy bands. Afterward, the Structural Similarity (SSIM) index and the k-means clustering algorithm were combined to select a few representative bands. Subsequently, the selected bands were injected into a supervised classifier, and the obtained Overall Accuracy (OA) and Kappa Coefficient (KC) were used to evaluate the performance of the proposed method. Finally, the results were compared with the ones achieved from other well-known and state-of-the-art FS approaches. The results revealed that the proposed method outperformed other FS algorithms. Furthermore, the proposed FS algorithm obtained equal or higher OA and KC in comparison with the case of employing all hyperspectral bands. Additionally, a stability analysis step was performed to investigate the consistency of the proposed method. The results suggest the potential of the FS approach for hyperspectral image classification.

scholarly journals Backlight Unit Excessive Dimming Based on Perceptual Image Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 1039
Author(s):  
Seungwoo Yang ◽  
Hyungsik Shin ◽  
JaeHee You
Keyword(s):  
Power Consumption ◽  
Visual System ◽  
Human Visual System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Opinion Score ◽  
The Mean ◽  
Mean Luminance ◽  
Index Measure ◽  
Contrast Response

A method to maximize backlight dimming of liquid crystal displays (LCDs) based on human visual system are proposed to minimize power consumption of display panels. Based on images, the proposed method optimizes global, local, and red, green, blue (RGB) backlight dimming by enhancing dimming about 12% on average for various images while maintaining tolerable degradation of perceived image qualities in pixel saturation areas. The method considers and utilizes the brightness sensitivity and contrast response functions of human visual system using the mean luminance and the contrast of an image, which are mathematically modelled to allow optimization for display panels and application areas. A simulator that can calculate various dimming cases with the evaluations of numerical and perceptual image qualities as well as power consumption amount is introduced. With pattern and real photo images, the degree of power savings and the preservation of image quality of the proposed method are verified to outperform conventional approaches with high scores of the mean opinion score (MOS) and the structural similarity index measure (SSIM) over 0.97 while saving more than 10% of power dissipation.

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.

scholarly journals GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1269
Author(s):  
Jiabin Luo ◽  
Wentai Lei ◽  
Feifei Hou ◽  
Chenghao Wang ◽  
Qiang Ren ◽  
...  
Keyword(s):  
Image Denoising ◽  
Data Augmentation ◽  
Noise Suppression ◽  
Random Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Convolutional Autoencoder

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

scholarly journals Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Aanzil Akram Halsana ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Mixture Model ◽  
Cancer Progression ◽  
Dirichlet Process ◽  
Network Inference ◽  
Correlation Method ◽  
Gaussian Mixture ◽  
Ductal Adenocarcinoma ◽  
Gene Regulatory Network Inference ◽  
Gene Modules

AbstractPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

scholarly journals Microwave Imaging of Breast Skin Utilizing Elliptical UWB Antenna and Reverse Problems Algorithm

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 647
Author(s):  
Sameer Alani ◽  
Zahriladha Zakaria ◽  
Tale Saeidi ◽  
Asmala Ahmad ◽  
Muhammad Ali Imran ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Imaging System ◽  
Signal Transmission ◽  
Similarity Index ◽  
Structural Similarity ◽  
Microwave Imaging ◽  
Ultra Wideband ◽  
Uwb Antenna ◽  
System A ◽  
High Structural Similarity

Skin cancer is one of the most widespread and fast growing of all kinds of cancer since it affects the human body easily due to exposure to the Sun’s rays. Microwave imaging has shown better outcomes with higher resolution, faster processing time, mobility, and less cutter and artifact effects. A miniaturized elliptical ultra-wideband (UWB) antenna and its semi-spherical array arrangement were used for signal transmission and reception from the defected locations in the breast skin. Several conditions such as various arrays of three, six, and nine antenna elements, smaller tumor, multi-tumors, and skin on a larger breast sample of 30 cm were considered. To assess the ability of the system, a breast shape container with a diameter of 130 mm and height of 60 mm was 3D printed and then filled with fabricated skin and breast fat to perform the experimental investigation. An improved modified time-reversal algorithm (IMTR) was used to recreate 2D images of tumors with the smallest radius of 1.75 mm in any location within the breast skin. The reconstructed images using both simulated and experimental data verified that the system can be a reliable imaging system for skin cancer diagnosis having a high structural similarity index and resolution.

Sign in / Sign up

Export Citation Format

Share Document