scholarly journals Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

2019 ◽  
Vol 9 (10) ◽  
pp. 1993 ◽  
Author(s):  
Ende Wang ◽  
Ping Jiang ◽  
Xukui Hou ◽  
Yalong Zhu ◽  
Liangyu Peng
Keyword(s):  
Wavelet Analysis ◽  
Infrared Imaging ◽  
Signal To Noise Ratio ◽  
Vertical Component ◽  
Infrared Image ◽  
Structural Similarity ◽  
Correction Algorithm ◽  
Uncooled Infrared Imaging ◽  
Correction Result ◽  
Better Than

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

Method for Uncooled Infrared Image Processing

2013 ◽  
Vol 427-429 ◽  
pp. 1948-1951
Author(s):  
Jia Lin Ma ◽  
Xia Zhang
Keyword(s):  
Image Processing ◽  
Infrared Imaging ◽  
Imaging System ◽  
Infrared Detector ◽  
Infrared Image ◽  
National Defense ◽  
Histogram Modification ◽  
Infrared Image Processing ◽  
Manufacturing Techniques ◽  
Uncooled Infrared Imaging

Uncooled infrared imaging system has been increasingly applied in both the national defense and various fields of national economy. Such popularity is attributed to many of its advantages, including small size, light weight, low energy-consumption and superior portability. However, as limited by the structure and the material of infrared detector and the manufacturing techniques, infrared images are plagued with low resolution and poor image quality. This paper mainly studies the uncooled infrared image processing based on the gray levels partition processing, gray levels stretching and histogram modification, it aims to enhance the visual effect of infrared image.

A New Method for Improving Infrared Image Quality and its System Implementation

2011 ◽  
Vol 130-134 ◽  
pp. 2989-2992
Author(s):  
Zhan Hua Huan ◽  
He Meng Yang ◽  
Meng Zhu
Keyword(s):  
Polynomial Interpolation ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Adaptive Threshold ◽  
High Signal ◽  
Piecewise Polynomial ◽  
Correction Algorithm ◽  
Enhancement Method ◽  
Enhancement Algorithm

To obtain high-quality infrared image real-timely, a new correction enhancement method is proposed. The method can both compensate nonuniformity of IRFPA by using calibration-based piecewise polynomial interpolation correction algorithm and increase image contrast by using histogram-based adaptive threshold image enhancement algorithm. The experiment is performed by carrying out the method in an embedded imaging system. The results show that the system can process infrared image real-timely and the processed image is clear with high signal to noise ratio.

Insulator Image Denoising Based on the Simplified PCNN Model

2013 ◽  
Vol 718-720 ◽  
pp. 2092-2098 ◽  
Author(s):  
Dan Li ◽  
Hong Ying He ◽  
Yi Jia Cao ◽  
Dian Sheng Luo
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Infrared Image ◽  
Window Size ◽  
Denoising Method ◽  
Similarity Calculation ◽  
Image Edge ◽  
Filter Noise ◽  
Better Than

A new denoising method was proposed in the paper according to the characteristics of insulator infrared image with impulse noise. First, based on the pulse coupled neural network (PCNN) to detect the location of the impulse noise pixels, while maintaining the same non-noise pixels. and then according to the characteristics of the impulse noise, the window size of the filter was adaptively determined by calculating the noise intensity of the image. The pixels with maximum and minimum gray value in filtering window are excluded, using the left pixels similarity calculation out weights. A new weighted filtering algorithm is used to filter noise pixels. The experiments show that the method is better than the median filter in peak signal-to-noise ratio (PSNR), and has better image edge details protection ability.

A COMPARISON BETWEEN SONOBUOY AND OCEAN BOTTOM SEISMOGRAPH DATA AND CRUSTAL STRUCTURE OF THE TEXAS SHELF ZONE

Geophysics ◽  
1978 ◽  
Vol 43 (3) ◽  
pp. 514-527 ◽  
Author(s):  
Abou‐Bakr K. Ibrahim ◽  
Gary V. Latham
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Vertical Component ◽  
Shelf Zone ◽  
Ocean Bottom ◽  
Low Velocity ◽  
Distance Curve ◽  
Resonant Frequencies ◽  
Ocean Bottom Seismograph ◽  
Better Than

Three refraction profiles were shot in the Gulf of Mexico 100 nautical miles south of Galveston. Recordings were made using free floating and anchored sonobuoys deployed near an ocean bottom seismograph (OBS). Explosions and airgun sources were used. Vertical component geophones with resonant frequencies of 4.5 Hz and 8 Hz were used in the OBS system. We compared seismic data recorded by the conventional sonobuoy and the OBS. The signal‐to‐noise ratio for the OBS is about three to four times better than that of the sonobuoy, facilitating the interpretation of the three profiles. Based on the first and later arrivals obtained from the OBS records, traveltime curves and an amplitude distance curve are constructed. The results show that the velocities at the experimental sites vary between 1.7 km/sec at the surface to 7.7 km/sec at a depth of 14.5 km. The 7.7 km/sec layer may be lower crust or anomalous mantle. The low velocity sediments (1.7 – 3.3 km/sec) thicken westward from the deployment point.

scholarly journals Denoising in Magnetic Resonance Images using Improved Gaussian Smoothing Technique

2019 ◽  
Vol 8 (2) ◽  
pp. 3693-3696
Keyword(s):  
Magnetic Resonance ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Smoothing Technique ◽  
Imaging Studies ◽  
Gaussian Smoothing ◽  
Better Than

Magnetic Resonance Images (MRI) are usually prone to noise like Rician and Gaussian noise. It is very difficult to perform image processing functions with the presence of noise. The objective of our work is to investigate the best method for denoising the MRI images. This study included 25 MRI subjects selected from the Open Access Series of Imaging Studies (OASIS) - 3 database. The 25 brain image subjects includes cases of both men and women aged 60 to 80. The input RGB image is first converted to gray scale image in which the contrast, sharpness, shadow and structure of the color of image are preserved. The proposed work uses an improved Gaussian smoothing technique for denoising Magnetic Resonance Images by constructing a modified mask for Gaussian smoothing. The performance of the proposed technique has been compared with various filters like median filter, Gaussian filter and Gabor filter. The performance evaluation was carried out by metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity (SSIM) index. The experimental results show that the Improved Gaussian Smoothing Technique (IGST) performs better than other methods. All experiments were conducted using Scikit Learn version 0.20 and Scikit Image version 0.14.1 under Python version 3.6.7.

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.

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.

Joint Encryption and Compression-Based Watermarking Technique for Security of Digital Documents

2021 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
A. K. Singh ◽  
S. Thakur ◽  
Alireza Jolfaei ◽  
Gautam Srivastava ◽  
MD. Elhoseny ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Digital Data ◽  
The Internet ◽  
Discrete Wavelet ◽  
Compression Performance ◽  
Strong Compression ◽  
Multiple Watermarks ◽  
Value Decomposition

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

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