Image Enhancement Using an Adaptive Un-sharp Masking Method Considering the Gradient Variation

Objective. To explore whether the image noises and the metal artifacts could be further managed by the combined use of two technologies, the adaptive statistical iterative reconstruction (ASIR) and the monochromatic imaging generated by gemstone spectral imaging (GSI) dual-energy CT. Materials and Methods. Fifty-one patients with 318 spinal pedicle screws were prospectively scanned with dual energy CT by using fast kV-switching GSI between 80 and 140 kVp. The monochromatic GSI images at 110 keV were reconstructed either without ASIR or with ASIR of various levels (30%, 50%, 70% and 100%). For these five sets of images, both objective and subjective image quality assessments were performed to evaluate the image quality. Results. With objective image quality assessment, the metal artifacts (measured by an artifacts index) significantly decreased when increasing levels of ASIR was utilized (p < 0.001). Moreover, adding ASIR to GSI also decreased the image noise (p < 0.001) and improved the signal-to-noise ratio (SNR, p < 0.001). With subjective image quality analysis, the inter-reader agreements were good, with intra-class correlation coefficients (ICC) of 0.89 to 0.99. Meanwhile, the visualization of the peri-implant soft tissue was improved at higher ASIR levels (p < 0.001). Conclusion. Combined use of ASIR and GSI is shown to decrease the image noise and improve the image quality in post-spinal fusion CT scans. Optimal results were achieved with ASIR levels of over 70%.

Existing objective and subjective TV image quality assessment metrics are considered. New metrics of digital image quality test are founded. Local entropy approach to form objective TV image quality assessment metrics is demonstrated experimentally.

SUMMARYTo restrain pseudo‐Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the nonsubsampled contourlet transform and the unsharp masking is proposed in this paper. The proposed method utilizes the shift‐invariance of nonsubsampled contourlet transform to restrain the pseudo‐Gibbs phenomenon, and then enhance details of the image by unsharp masking. We achieved an increase in image definition by 54.5%, the mean increased by 15.6%, whereas the standard deviation increased by 54.5% compared with the unsharp masking method. To the noisy image, we achieved an increase in image definition by 35.4%, the mean increased by 2.2%, whereas the standard deviation increased by 34.9% compared with the unsharp masking method. Copyright © 2013 John Wiley & Sons, Ltd.

In medical ultrasound B-mode imaging, by increasing the image contrast, spatial and temporal resolution, it will help to improve the diagnostic and decision making process. Previously the unsharp masking (UM) techniques have been successfully implemented for digital image processing as edge enhancement techniques. However, the outcomes of the method are limited and influenced by the image formats conversion and lower dynamic range. Moreover the conventional UM technique sensitive to noise due to the presence of linear high pass filter which cannot discriminate signal from noise. Furthermore, the technique only enhances the image in darker compared to lighter regions. To overcome those issues, a new improved UM method compatible with ultrasound B-mode imaging was applied to compound plane wave imaging (CPWI). The received radio frequency (RF) signal was beamformed with delay and sum (DAS) and filtered delay and sum (FDMAS). Results show that the proposed techniques are not only able to improve the image contrast but also the spatial resolution. The UM technique manages to improve the B-mode image lateral resolution (LR) by 18.36% and 10.25% as well as reduced the peak side lobes (PSL) by 16.67 dB and 21.24 dB on DAS and FDMAS respectively when compounded with CPWI, N=13. For the same number of compounding and beamforming order, the image contrast ratio (CR) has been improved by 10.35 dB and 7.39 dB accordingly.

Shift-and-add (SAA) is a simple image processing procedure. SAA was devised to reconstruct a diffraction-limited image from atmospherically degraded stellar images. Recently SAA has been applied to biological imaging. There are several variants of SAA. Here proposed is an SAA procedure incorporated with unsharp masking (USM). The SAA procedure proposed here encompasses an extended version of USM. The proposed SAA method retains the simplicity and easiness, and the basic features of SAA. The effectiveness of the proposed method is examined by restoring atmospherically degraded solar images. It is shown that the USM SAA reconstructed image exhibits high contrast and reveals fine structures blurred by atmospheric turbulence. It is also shown that the USM SAA performs better with a data frame selection scheme.

To compare single-detector row computed tomography (CT) and multi-detector row CT by using an experimental phantom containing a contrast-detail modulus for detection of low-contrast structures to optimize acquisition protocols. The parameters milliampere seconds, reconstructed section thickness, and elementary collimation for multi-detector CT were varied for two pitches with single- and multi-detector CT. For objective assessment of image quality, contrast-to-noise ratio (CNR) was calculated for an 8-HU low-contrast 15-mm-diameter object. Subjective assessment of image quality was performed by means of visual detection of low-contrast objects of various sizes by four independent observers. For each acquisition protocol, the effective doses required to obtain the CNR thresholds allowing 100% detection of 5-, 7-, and 9-mm-diameter objects were compared at single- and multi-detector CT at comparable section sensitivity profile with analysis of variance. Significant correlation was found between CNR measurements and subjective object detection (r = 0.95, P <.05). CNRs of 1.0, 0.8, and 0.6 were required to detect 100% of the 5-, 7-, and 9-mm-diameter objects, respectively. For reconstructed section thickness of 5-10 mm, comparable x-ray doses were required with single- and multi-detector CT to detect objects. For reconstructed sections thinner than 5 mm, single- and multi-detector CT allowed detection of only the 7- and 9-mm-thick objects, but a higher x-ray dose was required for multi- than for single-detector CT (P <.05). Multi-detector CT is less effective than single-detector CT in detection of small low-contrast objects if sections thinner than 5 mm are used. Results for single- and multi-detector CT were similar for sections 5 mm or thicker.

The motive of palmprint image enhancement is to highlight the fine details of palm lines. In this paper, a fuzzy unsharp masking algorithm is presented to enhance the contrast of a palmprint image. This method introduces the fuzzy set theory into the unsharp masking scheme. Unsharp masking (UM) approaches are simple and effective for contrast enhancement of high-frequency components. Most of UM algorithms can effectively improve the contrast of principal lines, but insensitive to wrinkles. In order to further enhance the contrast of wrinkles, a half open membership function is used to transform the output of the filters into fuzzy domain. The palmprint image is enhanced in fuzzy field. The proposed method is compared with other techniques in the paper and the experimental result demonstrates that the proposed approach can enhance the fine details of palmprint image effectively.

Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented

Digitized hand-wrist radiographs: Comparison of subjective and software-derived image quality at various compression ratios

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

Fuzzy regression for perceptual image quality assessment

This study evaluates the image quality of postmortem photon-counting computed tomography (PMPCCT) compared to conventional postmortem energy-integrating computed tomography (PMCT) for a protocol adapted to post-mortem imaging. The focus lies on objective image quality parameters such as noise, signal-to-noise ratio, and contrast-to-noise ratio, supplemented by subjective image quality evaluations.A Chest Phantom N1 and one decedent were scanned using PMCT (Siemens SOMATOM® X-Cite; slice thicknesses of 1mm and 0.5mm; Br40, Br60) and PMPCCT (Siemens NAEOTOM Alpha.peak®; slice thicknesses of 1mm, 0.4mm, and 0.2mm; Br40, Br60). Image quality parameters were computed for regions of interest. In addition, two radiologists conducted subjective image quality evaluations (noise, overall image quality, sharpness, bone details, contour visibility, and artifact formation) using a modified Likert scale.The overall findings were mixed, with PMPCCT potentially demonstrating an advantage over PMCT in terms of SNR and CNR, particularly at moderate slice thicknesses of 1mm and 0.4mm. The results obtained from the phantom exhibit in some cases considerable differences compared to those from the decedent, so that model studies can hardly be transferred to reality without scrutiny. PMPCCT outperformed PMCT in subjective assessments of overall image quality, sharpness, and bone detail, particularly with Br40 reconstruction kernels.PMPCCT may demonstrate advantages in objective and subjective image quality, with improvements in soft tissue imaging, sharpness, and bone detail at moderate slice thicknesses. These results may suggest PMPCCTs promising potential in forensic imaging. Further studies are needed to investigate and optimize its utility in postmortem settings.

It has been observed that enhancing a highly-blurred image region could often lead to unpleasant noise amplification. Motivated by this, an adaptive unsharp masking (UM) method is proposed in this paper, which incorporates the estimated local blurriness information into the enhancement process to adaptively determine the scaling factor for each pixel on the detail layer. To achieve this goal, a pixel-wise local blurriness estimation method is developed for generating a pixel-wise blurriness map, followed by individually converting each blurriness measurement on the map to a scaling factor via a mapping process. The proposed method not only avoids noise amplification in blurred regions but also addresses ‘high-level’ considerations, such as photographer's original intention on making background more blurred for creating special aesthetic effect. Extensive simulations conducted on various test images have demonstrated that our approach is able to deliver much superior perceptual quality of enhanced images compared to other state-of-the-art UM methods.

Medical image enhancement is an effective tool to improve visual quality of digital medical images. In this paper, we propose a new unsharp masking scheme for medical image enhancement. It embeds the PLIP multiplication into the unsharp masking framework. Experimental results demonstrate that the proposed method can effectively enhance the overall contrast and edges of medical images while suppressing background noise.

Introduction: Today film-based technologies are largely abandoned in dental radiology. New technologies based on digital detectors are being adopted. This change is part of the global digitalization of radiological procedures. A person operating the radiological device needs to be educated and well informed about radiological procedures and technologies, as well as to know how the system operates and to be sure about the dose required for the adequate image quality. The awareness of radiation doses received by patients is necessary for the estimation of risk from ionizing radiation.Material and methods: The aim of this study was to evaluate whether patient doses in intraoral dental radiology affect image quality. The study included subjective analysis of radiological image quality for an intraoral x-ray device with possibility to use both digital and film detector. Subjective assessment of image quality is performed according to criteria taken from the literature and is expressed using the Likert scale, grade 1–5. In order to improve the quality of inferential statistics related ratings to detector quality and image quality were collected, thus introducing two variables: the detector quality index and the technique quality index.Results: Z-test proportions column shows that the number of grades four given as a contrast score is significantly higher in the RVG detector (65%) compared to the film (40%). There is also a significant difference in the number of given grades five as a rating of the apex preview. The share of grades five in film is 19%, while in RVG it is 42%. It is similar in the evaluation of the preview of the dental canal, where there is also a significant difference in the number of given grades five, so that the share in the film is 11% and in RVG 35%.Conclusion: Analysis of the subjective quality of the radiological image in devices for intraoral radiography with the possibility of recording using digital and film detectors indicates that the digital detector is superior to film in low contrast resolution, apex and dental canal preview.