scholarly journals Iterative Phase-Only Hologram Generation Based on the Perceived Image Quality

2019 ◽  
Vol 9 (20) ◽  
pp. 4457 ◽  
Author(s):  
Haining Yang ◽  
Daping Chu
Keyword(s):  
Image Quality ◽  
Mean Squared Error ◽  
Quality Metrics ◽  
Critical Element ◽  
Image Content ◽  
Quality Metric ◽  
Squared Error ◽  
Image Quality Metrics ◽  
Image Quality Metric

Image quality metrics are a critical element in the iterative Fourier transform algorithms (IFTAs) for the computer generation of phase-only holograms. Conventional image quality metrics such as root-mean-squared error (RMSE) are sensitive to image content and unable to reflect the perceived image quality accurately. This would have a significant impact on the calculation speed and the quality of the generated hologram. In this work, the structure similarity (SSIM) was proposed as an image quality metric in IFTAs due to its ability to predict the perceived image quality in the presence of the white Gaussian noise and its independence on the image content. This would enable IFTAs to terminate when further iterations would no longer lead to improvement in the perceived image quality. As a result, up to 75% of unnecessary iterations were eliminated by the use of SSIM as the image quality metric.

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

scholarly journals Repeatability of a Dual-Scheimpflug Placido Disc Corneal Tomographer/Topographer in Eyes with Keratoconus

2020 ◽  
Author(s):  
Anne Poulsen ◽  
Diane Jang ◽  
Mahmood Khan ◽  
Zaina Nabil Al-Mohtaseb ◽  
Michael Chen ◽  
...  
Keyword(s):  
Image Quality ◽  
Spherical Aberration ◽  
Correlation Coefficients ◽  
Quality Metrics ◽  
Corneal Surgery ◽  
Image Quality Metrics ◽  
Interclass Correlation ◽  
Intrastromal Corneal Ring ◽  
History Of ◽  
Image Quality Metric

Purpose: To investigate the repeatability of a combined Dual-Scheimpflug placido disc corneal topographer (Zeimer Galilei G4) with respect to keratometric indices used to monitor progression of keratoconus (KCN). Methods: Patients with KCN were prospectively enrolled. For each eye lacking history of corneal surgery, 5 measurements were taken in succession. Eyes in which 3 or more measurements could be obtained (defined by the device's 4 image quality metrics) were included in the analysis. The repeatability limits (RL) and interclass correlation coefficients (ICC) were calculated for various parameters. Results: 32 eyes from 25 patients met all image quality metrics, and 54 eyes from 38 patients met at least 3/4 criteria (all except the placido image quality metric). RLs for key parameters when 4/4 or 3/4 image quality metrics were met included: 0.37 and 0.77 diopters (D) for steep simulated keratometry, 0.79 and 1.65 D for maximum keratometry, 13.80 and 13.88 degrees for astigmatism axis, 0.64 and 0.56 um for vertical coma magnitude, and 3.76 and 3.84 um for thinnest pachymetry, respectively. The ICCs for all parameters were excellent [above 0.87 except for spherical aberration (0.77)]. Conclusions: The dual-Scheimpflug placido disc corneal topographer is highly repeatable in quantifying parameters used in monitoring KCN. Excellent placido images are difficult to capture in eyes with KCN, but when available, increase the reliability of the measurements. The RLs may be especially helpful in detecting progression in mild KCN when interventions such as corneal cross-linking or intrastromal corneal ring segments are most beneficial.

Colourlab Image Database: Geometric Distortions

2021 ◽  
Vol 2021 (29) ◽  
pp. 258-263
Author(s):  
Marius Pedersen ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Performance ◽  
Image Database ◽  
Quality Metrics ◽  
Current Image ◽  
Geometric Distortions ◽  
Image Quality Metrics ◽  
Objective Metrics ◽  
Reference Images

Over the years, a high number of different objective image quality metrics have been proposed. While some image quality metrics show a high correlation with subjective scores provided in different datasets, there still exists room for improvement. Different studies have pointed to evaluating the quality of images affected by geometrical distortions as a challenge for current image quality metrics. In this work, we introduce the Colourlab Image Database: Geometric Distortions (CID:GD) with 49 different reference images made specifically to evaluate image quality metrics. CID:GD is one of the first datasets which include three different types of geometrical distortions; seam carving, lens distortion, and image rotation. 35 state-ofthe-art image quality metrics are tested on this dataset, showing that apart from a handful of these objective metrics, most are not able to show a high performance. The dataset is available at <ext-link ext-link-type="url" xlink:href="http://www.colourlab.no/cid">www.colourlab.no/cid</ext-link>.

scholarly journals Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

2021 ◽  
Vol 11 (10) ◽  
pp. 4661
Author(s):  
Aladine Chetouani ◽  
Marius Pedersen
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Feature Vector ◽  
Pearson Correlation ◽  
Image Database ◽  
Viewing Distance ◽  
Quality Metric ◽  
Image Quality Metric ◽  
Fully Connected

An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.

scholarly journals Comparison of Objective Image Quality Metrics to Expert Radiologists’ Scoring of Diagnostic Quality of MR Images

2020 ◽  
Vol 39 (4) ◽  
pp. 1064-1072 ◽  
Author(s):  
Allister Mason ◽  
James Rioux ◽  
Sharon E. Clarke ◽  
Andreu Costa ◽  
Matthias Schmidt ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Metrics ◽  
Mr Images ◽  
Diagnostic Quality ◽  
Image Quality Metrics

Overview of Image Quality Metrics with Perspective to Satellite Image Compression

2016 ◽  
Vol 24 ◽  
pp. 112-123
Author(s):  
S. Sanjith ◽  
R. Ganesan
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Satellite Image ◽  
Visual Quality ◽  
Quality Metrics ◽  
Human Beings ◽  
Picture Processing ◽  
Image Quality Metrics ◽  
Satisfactory Answer

Image Quality appraisal has been an exacting task in the field of image processing without any satisfactory answer so far. Image quality evaluation tries to quantify a visual quality, an amount of distortion in a given picture. These changes are an inescapable component of any digital picture processing. The correct method of valuing the human-perceived visual quality of the images is the assessment by the human beings. Unfortunately, this process is luxurious, time consuming and cannot be applied in real-time applications. Therefore, there is a demand for a computerized technique that would conceive of the human-perceived visual quality as close as possible. This survey presents an overview about different quality metrics used in-order to assess the image degradation. The few metrics studied are MSE, SNR, PSNR, SSIM, AD, MD, MAE, NK, VSNR, RMSE, UIQM, MSSSIM, FSSIM etc. The image quality metrics are verified with perspective to satellite pictures.

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric
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