scholarly journals Multifocus Images Fusion Based On Homogenity and Edges Measures

2014 ◽  
Vol 11 (2) ◽  
pp. 660-672
Author(s):  
Baghdad Science Journal
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Signal To Noise Ratio ◽  
Evaluation Process ◽  
Statistical Techniques ◽  
Signal To Noise ◽  
High Quality ◽  
Simulation Process ◽  
Statistical Criteria

Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM, RVS and WT) and spatial techniques (HPFA, HFA and HFM). As these techniques have been developed and build programs using the language MATLAB (b 2010). In this work homogeneity criteria have been suggested for evaluation fused digital image's quality, especially fine details. This criterion is correlation criteria to guess homogeneity in different regions within the image by taking a number of blocks of different regions in the image and different sizes and work shifted blocks per pixel. As dependence was on traditional statistical criteria such as (mean, standard deviation, and signal to noise ratio, mutual information and spatial frequency) and compared with the suggested criteria to the work. The results showed that the evaluation process was effective and well because it took into measure the quality of the homogenous regions.

scholarly journals Super-Resolution of Digital Images using CNN with Leaky ReLU

2019 ◽  
Vol 8 (2S11) ◽  
pp. 210-212 ◽  
Keyword(s):  
Digital Images ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Resolution ◽  
Signal To Noise ◽  
Analysis Process ◽  
Real World Applications ◽  
Image Super Resolution ◽  
Leaky Rectified Linear Unit

Image super-resolution (SR) has been used in many real world applications as a preprocessing phase. The improvement in image resolution increases the performance of image analysis process. The SR of digital images is achieved by taking the low resolution images as inputs. In this article, a novel deeplearning based super-resolution approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with test images taken from USC-SIPI dataset indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio.

scholarly journals Faktor Eksposi terhadap Kualitas Citra Radiografi dan Dosis Pasien Menggunakan Parameter Penilaian Signal to Noise Ratio (SNR) pada Pemeriksaan Thorax Posteroanterior dengan Menggunakan Pesawat Computed Radiografi

2021 ◽  
Vol 7 (1) ◽  
pp. 15-18
Author(s):  
Surdiyah Asriningrum ◽  
Khaerul Ansory ◽  
Putra Tri Hasan
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Computed Radiography ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Measurement Results ◽  
Noise Ratio ◽  
Dose Radiation ◽  
Average Body

Background: The research was analyzing digital image quality and estimation dose patient by using  Signal to Noise Ratio (SNR) on Computed Radiography. SNR can be used for analyzing digital image spatial resolution and estimation dose accurately. The aims of this study to determine the influence of exposure factors on image quality and estimation dose patient.Methods: This type of research is a quantitative method with an experimental study. Direct experiments in August 2020 assessment with a sample of 9 adults posteroanterior chest photo with the average age of 20-50 years old with an average body weight of 50-69 kilograms. Results: The measurement results showed that the digital images will be analyzed by SNR, so it can be determined the optimum exposed factor of the highest SNR value and dose radiation. From the analysis, the highest SNR value at 121 kV, current 1 mAs, the lower dose radiation at 121 kV, current 0,9 mAs.Conclusions: There was an influence variation of an exposed factor on the quality of the image and dose to the patient.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Short-pulse laser-driven x-ray radiography

2016 ◽  
Vol 4 ◽  
Author(s):  
E. Brambrink ◽  
S. Baton ◽  
M. Koenig ◽  
R. Yurchak ◽  
N. Bidaut ◽  
...  
Keyword(s):  
Pulse Laser ◽  
Short Pulse ◽  
Incident Angle ◽  
Laboratory Astrophysics ◽  
Signal To Noise ◽  
High Quality ◽  
X Ray ◽  
Short Pulse Laser ◽  
Short Pulse Lasers

We have developed a new radiography setup with a short-pulse laser-driven x-ray source. Using a radiography axis perpendicular to both long- and short-pulse lasers allowed optimizing the incident angle of the short-pulse laser on the x-ray source target. The setup has been tested with various x-ray source target materials and different laser wavelengths. Signal to noise ratios are presented as well as achieved spatial resolutions. The high quality of our technique is illustrated on a plasma flow radiograph obtained during a laboratory astrophysics experiment on POLARs.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

2012 ◽  
Vol 29 (6) ◽  
pp. 772-795 ◽  
Author(s):  
Lei Lei ◽  
Guifu Zhang ◽  
Richard J. Doviak ◽  
Robert Palmer ◽  
Boon Leng Cheong ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Data Quality ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Signal To Noise ◽  
Spectral Moments ◽  
Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

Learning Aided Digital Image Compression Technique for Medical Application

2016 ◽  
pp. 400-422
Author(s):  
Kandarpa Kumar Sarma
Keyword(s):  
Image Compression ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Medical Application ◽  
Learning Systems ◽  
Signal To Noise ◽  
Compression Technique ◽  
Compressed Images ◽  
Digital Image Compression ◽  
Efficiency And Reliability

The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.

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