scholarly journals An Image Quality Evaluation Model for Optical Strip Signal-to-Noise Ratio in the Target Area of High Temperature Forgings

2021 ◽  
Vol 8 (2) ◽  
pp. 93-100
Author(s):  
Hongtao Ma ◽  
Yuyang Zhao ◽  
Yiran Feng ◽  
Eung-Joo Lee ◽  
Xueheng Tao
Keyword(s):  
High Temperature ◽  
Image Quality ◽  
Quality Evaluation ◽  
Signal To Noise Ratio ◽  
Evaluation Model ◽  
Target Area ◽  
Signal To Noise ◽  
Image Quality Evaluation ◽  
Noise Ratio ◽  
Quality Evaluation Model

Image Quality Evaluation Model Based on Local Features and Segmentation

2006 ◽  
Author(s):  
Yuukou Horita ◽  
Masaharu Sato ◽  
Yoshikazu Kawayoke ◽  
Z. M. Parvez Sazzad ◽  
Keiji Shibata
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Local Features ◽  
Image Quality Evaluation ◽  
Model Based ◽  
Quality Evaluation Model

3D Image Quality Evaluation Model in Stereoscopic Imaging Technology

2013 ◽  
Vol 19 (2) ◽  
pp. 449-454
Author(s):  
Wan-Young Chung ◽  
Rohit Ramesh ◽  
Shin-ll Jeong ◽  
Gi-Sik Byun
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
3D Image ◽  
Imaging Technology ◽  
Image Quality Evaluation ◽  
Stereoscopic Imaging ◽  
Quality Evaluation Model

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

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

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.

Preliminary study of improved median filter using adaptively mask size in light microscopic image

Microscopy ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 31-36
Author(s):  
Ji-Youn Kim ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Coefficient Of Variation ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Microscopic Image ◽  
Signal To Noise ◽  
Mask Size ◽  
Preliminary Study ◽  
Noise Ratio ◽  
Image Details

Abstract This study aimed to develop and evaluate an improved median filter (IMF) with an adaptive mask size for light microscope (LM) images. We acquired images of the mouse first molar using a LM at 100× magnification. The images obtained using our proposed IMF were compared with those from a conventional median filter. Several parameters such as the contrast-to-noise ratio, coefficient of variation, no-reference assessments and peak signal-to-noise ratio were employed to evaluate the image quality quantitatively. The results demonstrated that the proposed IMF could effectively de-noise the LM images and preserve the image details, achieving a better performance than the conventional median filter.

scholarly journals Image multiplexing using residue number system coding over MIMO-OFDM communication system

2019 ◽  
Vol 9 (6) ◽  
pp. 4815
Author(s):  
Mohamed Ibrahim Youssif ◽  
Amr ElSayed Emam ◽  
Mohamed Abd ElGhany
Keyword(s):  
Image Quality ◽  
Communication System ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Number System ◽  
Residue Number System ◽  
Signal To Noise ◽  
Residue Number ◽  
Mimo Ofdm ◽  
Noise Ratio

<p>Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.</p><p> </p>

Sign in / Sign up

Export Citation Format

Share Document