scholarly journals Single Image Rain Removal Based on Deep Learning and Symmetry Transform

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 224
Author(s):  
Qing Yang ◽  
Ming Yu ◽  
Yan Xu ◽  
Shixin Cen
Keyword(s):  
Deep Learning ◽  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Weather Condition ◽  
Structural Similarity ◽  
Threshold Value ◽  
Source Image ◽  
Inverse Transformation ◽  
Single Image ◽  
Rain Removal

Rainy, as an inevitable weather condition, will affect the acquired image. To solve this problem, a single image rain removal algorithm based on deep learning and symmetric transformation is proposed. Because of the important characteristics of wavelet transform, such as symmetry, orthogonality, flexibility and limited support, wavelet transform is used to remove rain from a single image. The image is denoised by using wavelet decomposition, threshold value and wavelet reconstruction in wavelet transform, and the rain drop image is transformed from RGB space to YUV (luma chroma) space by using deep learning to obtain the brightness component and color component of the image. the brightness component and residual component of the raindrop source image and the ideal recovered image without raindrop are extracted. The residual image and brightness component are overlapped again, the reconstructed image is restored to RGB space by YUV inverse transformation, and the final color raindrop free image is obtained. After training the network, the optimal parameters of the network are obtained, and finally the convolution neural network which can effectively remove the rain line is obtained. Experimental results show that compared with other algorithms, the proposed algorithm achieves the highest value in both peak signal-to-noise ratio (PSNR) and structural similarity, which shows that the image effect of the algorithm is better after rain removal.

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 Computing 3D Phase-Type Holograms Based on Deep Learning Method

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 280
Author(s):  
Huadong Zheng ◽  
Jianbin Hu ◽  
Chaojun Zhou ◽  
Xiaoxi Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Contrast Ratio ◽  
Angular Spectrum ◽  
Structural Similarity ◽  
Computational Time ◽  
Experimental Investigations ◽  
Training Strategy ◽  
Holographic Display ◽  
Phase Type

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

scholarly journals Lifting Wavelet Transform in Compressive Sensing for MRI Reconstruction

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Indrarini Dyah Irawati ◽  
Sugondo Hadiyoso ◽  
Gelar Budiman ◽  
Asep Mulyana
Keyword(s):  
Wavelet Transform ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Lifting Wavelet Transform ◽  
Structural Similarity Index ◽  
Compressed Sampling ◽  
Lifting Wavelet

Compressed sampling in the application of magnetic resonance imaging compression requires high accuracy when reconstructing from a small number of samples. Sparsity in magnetic resonance images is a fundamental requirement in compressed sampling. In this paper, we proposed the lifting wavelet transform sparsity technique by taking wavelet coefficients on the low pass sub-band that contains meaningful information. The application of novel methods useful for compressing data with the highest compression ratio at the sender but still maintaining high accuracy at the receiver. These wavelet coefficient values are arranged to form a sparse vector. We explore the performance of the proposed method by testing at several levels of lifting wavelet transform decomposition, include Levels 2, 3, 4, 5, and 6. The second requirement for compressed sampling is the acquisition technique. The data sampled sparse vectors using a normal distributed random measurement matrix. This matrix is normalized to the average energy of the image pixel block. The last compressed sampling requirement is a reconstruction algorithm. In this study, we analyze three reconstruction algorithms, namely Level 1 magic, iteratively reweighted least squares, and orthogonal matching pursuit, based on structural similarity index measured and peak signal to noise ratio metrics. Experimental results show that magnetic resonance imaging can be reconstructed with higher structural similarity index measured and peak signal to noise ratio using the lifting wavelet transform sparsity technique at a minimum decomposition level of 4. The proposed lifting wavelet transforms and Level 1 magic reconstruction algorithm has the best performance compared to the others at the measurement rate range between 10 to 70. This method also outperforms the techniques in previous studies.

scholarly journals Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care

2021 ◽  
Author(s):  
S. Bash ◽  
B. Johnson ◽  
W. Gibbs ◽  
T. Zhang ◽  
A. Shankaranarayanan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Standard Of Care ◽  
Structural Similarity ◽  
Image Features ◽  
Scan Time ◽  
Magnetic Resonance Imaging Mri ◽  
Display Order ◽  
Spine Mri

Abstract Objective This prospective multicenter multireader study evaluated the performance of 40% scan-time reduced spinal magnetic resonance imaging (MRI) reconstructed with deep learning (DL). Methods A total of 61 patients underwent standard of care (SOC) and accelerated (FAST) spine MRI. DL was used to enhance the accelerated set (FAST-DL). Three neuroradiologists were presented with paired side-by-side datasets (666 series). Datasets were blinded and randomized in sequence and left-right display order. Image features were preference rated. Structural similarity index (SSIM) and per pixel L1 was assessed for the image sets pre and post DL-enhancement as a quantitative assessment of image integrity impact. Results FAST-DL was qualitatively better than SOC for perceived signal-to-noise ratio (SNR) and artifacts and equivalent for other features. Quantitative SSIM was high, supporting the absence of image corruption by DL processing. Conclusion DL enables 40% spine MRI scan time reduction while maintaining diagnostic integrity and image quality with perceived benefits in SNR and artifact reduction, suggesting potential for clinical practice utility.

scholarly journals Denoising Images by Dual–Tree Complex Wavelet Transform Combined with Meta Heuristic Optimization Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 2621-2627
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Optimization Algorithms ◽  
Optimization Technique ◽  
Threshold Value ◽  
Heuristic Optimization ◽  
Algorithm Optimization ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

Denoising is a prime objective technique for processing images. Image denoising techniques removes the noises present in an image without interrupting its features and contents. The image gets interrupted by channel or processing noise depending on the applications. Thus, the contaminated noises produce degradable image qualities with respect to subjective and objective approach. To overcome this, image denoising approaches were suggested. In the present research, Dual–Tree Complex Wavelet transform (DTCWT) is utilized to achieve image denoising since they perform multi resolution decomposition by two DWT trees. Soft and hard thresholding methods are used to threshold wavelet coefficients. The present research proposes a novel technique to denoise images which gives image information clearly by thresholding and optimization technique. The optimization is carried through different Meta-heuristic optimization Algorithms Genetic Algorithm (GA) and Grey-wolf optimization (GWO) algorithm. Optimization of threshold value is performed after Bayesian method and the observed output produces better results when compared to other techniques involving Visu shrink, Sure shrink and Bayes shrinkbased on peak signal to noise ratio (PSNR) and visual qualities.

scholarly journals Dual tree complex wavelet transform based image denoising for Kalpana satellite images

2018 ◽  
Vol 7 (3.29) ◽  
pp. 269
Author(s):  
Naga Lingamaiah Kurva ◽  
S Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Pictorial Representation ◽  
Complex Wavelet Transform ◽  
Raw Data ◽  
Complex Wavelet ◽  
Kalpana Satellite

This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.   

Morphological based Medical Image Processing on Cervical Cytology Cancer Images using Connected Component Techniques

2021 ◽  
Vol 17 ◽  
Author(s):  
P. Robert ◽  
A. Celine Kavida
Keyword(s):  
Cervical Cancer ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Threshold Value ◽  
Good Precision ◽  
Connected Component ◽  
Binary Images ◽  
Proposed Model ◽  
Structural Content

: Cervical cancer is the fourth most common cancer in women. In 2018, it was estimated that 570000 women were diagnosed with cervical cancer worldwide, and about 311000 women died from the disease. An efficient technique is essential for solving the complication in the diagnosis of cervical cancer images. In this research, a new method is developed for cervical cancer image segmentation. First, the RGB image is converted into an HSI color model. Then, the thresholding is applied to the saturation and intensity components to get binary images. These binary images are combined to get a new mask. Using the connected component concept, nucleus and cytoplasm are segmented accurately. For the performance evaluation, peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM), image quality index (IQI), structural content (SC), normalized cross-correlation (NK), precision (PR), recall (RC), the average difference (AD), and image fidelity (IF) are taken. The proposed techniques’ highest PSNR values are 44.2341, 46.7953, 60.5925, and 61.4862, respectively. The proposed segmentation technique can attain a high PSNR (>40db) value at a threshold value equal to 0.1. Also proposed approach attains good precision, recall, and SSIM values. The lowest MSE values using proposed segmentation techniques are 0.0454, 0.0351, 0.0924, and 0.0271 individually. The AD, NK, SC, NAE, and LMSE values for the implemented approach are low, showing that the segmented image’s quality is very good. Thus, the proposed model performed better compared to other methods.

Single-Image Rain Removal Using Residual Deep Learning

2018 ◽  
Author(s):  
Takuro Matsui ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Deep Learning ◽  
Single Image ◽  
Rain Removal

IMAGE DE-NOISING USING DOUBLE DENSITY WAVELET TRANSFORM BASED ADAPTIVE THRESHOLDING TECHNIQUE

2005 ◽  
Vol 03 (01) ◽  
pp. 141-152 ◽  
Author(s):  
D. GNANADURAI ◽  
V. SADASIVAM
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Adaptive Method ◽  
Threshold Estimation ◽  
Statistical Parameters ◽  
Geometrical Mean ◽  
Optimum Threshold ◽  
Superior Image Quality

This paper describes an efficient and adaptive method of threshold estimation for removing impulse noise from images, based on Double Density Wavelet Transform (DDWT). The performance of image de-noising algorithms using wavelet transforms can be improved significantly by fixing an optimum threshold value, based on the analysis of the statistical parameters of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. Here the noisy image is first decomposed into many levels to obtain different frequency bands using DDWT. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum threshold value by the proposed method. Experimental results on several test images by using the proposed method show that, the proposed method yields significantly superior image quality and better Peak Signal-to-Noise Ratio (PSNR). Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.

scholarly journals Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

2021 ◽  
Vol 7 (3) ◽  
pp. 44
Author(s):  
Johannes Leuschner ◽  
Maximilian Schmidt ◽  
Poulami Somanya Ganguly ◽  
Vladyslav Andriiashen ◽  
Sophia Bethany Coban ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Measurement Model ◽  
Training Data ◽  
Data Driven ◽  
Reconstruction Methods ◽  
Reconstruction Quality ◽  
Public Datasets

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

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