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- 摘要
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- 实验方案
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Endoscopic image enhancement with noise suppression
摘要： Stereoscopic endoscopes have been used increasingly in minimally invasive surgery to visualise the organ surface and manipulate various surgical tools. However, insuf?cient and irregular light sources become major challenges for endoscopic surgery. Not only do these conditions hinder image processing algorithms, sometimes surgical tools are barely visible when operating within low-light regions. In addition, low-light regions have low signal-to-noise ratio and metrication artefacts due to quantisation errors. As a result, present image enhancement methods usually suffer from heavy noise ampli?cation in low-light regions. In this Letter, the authors propose an effective method for endoscopic image enhancement by identifying different illumination regions and designing the enhancement design criteria for desired image quality. Compared with existing image enhancement methods, the proposed method is able to enhance the low-light region while preventing noise ampli?cation during image enhancement process. The proposed method is tested with 200 images acquired by endoscopic surgeries. Computed results show that the proposed algorithm can outperform state-of-the-art algorithms for image enhancement, in terms of naturalness image quality evaluator and illumination index.
关键词： image quality,endoscopic image enhancement,noise suppression,minimally invasive surgery,illumination regions
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Guangzhou, China (2018.10.8-2018.10.12)] 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks
摘要： Terahertz human body security images have low resolution and a low signal-to-noise ratio. In the traditional method, image segmentation, positioning, and identification are applied to detect objects carried by humans in the THz images. However, it is difficult to satisfy the requirements of detection accuracy and speed with this approach. The current research presents a faster detection framework (R-PCNN) combining the preprocessing and structure optimization of Faster R-CNN. The experiment results show that this method can effectively improve the accuracy and speed of object detection in human body THz images. A detection accuracy of 84.5% can be achieved in dense flow scenes, with an average detection time of less than 20 milliseconds for each image.
关键词： Image enhancement,Terahertz image,Faster R-CNN,Human body security check,Object detection
更新于2025-09-04 15:30:14
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[Lecture Notes in Electrical Engineering] Recent Trends in Communication, Computing, and Electronics Volume 524 (Select Proceedings of IC3E 2018) || Image Quality Assessment: A Review to Full Reference Indexes
摘要： An image quality index plays an increasingly vital role in image processing applications for dynamic monitoring and quality adjustment, optimization and parameter setting of the imaging systems, and finally benchmarking the image processing techniques. All the above goals highly require a sustainable quantitative measure of image quality. This manuscript analytically reviews the popular reference-based metrics of image quality which have been employed for the evaluation of image enhancement techniques. The efficiency and sustainability of eleven indexes are evaluated and compared in the assessment of image enhancement after the cancellation of speckle, salt and pepper, and Gaussian noises from MRI images separately by a linear filter and three varieties of morphological filters. The results indicate more clarity and sustainability of similarity-based indexes. The direction of designing a universal similarity-based index based on information content of the image is suggested as a future research direction.
关键词： Image quality measurement,Noise cancellation,Similarity measurement,Error measurement,Image enhancement
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Shape-aware Medical Image Enhancement by Weighted Total Variation
摘要： In this paper, we propose a sharpening method for medical images combining weights on each pixel and Total Variation regularization. When weighting properly on each pixel in Total Variation regularization, information loss in optimization can be prevented. In the proposed method, weighting for each pixel is calculated from emphasis processing on an image and edge information, and Total Variation regularization is performed using weight information. As a result, noise removal and sharpening can be performed at the same time. Moreover, by comparing the proposed method with the conventional method, qualitative evaluation is carried out, and the effectiveness is shown.
关键词： Image enhancement,Image denoising,Medical diagnostic imaging,Biomedical imaging
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - A Low-Light Color Image Enhancement Method on CIELAB Space
摘要： A low-light color image enhancement method on CIELAB uniform color space is presented in this paper. First, the RGB color space of the input image is transformed into the CIELAB color space. Then, the intensity component on CIELAB space is amplified by using the power law transform (PLT). Next, an image fusion method is used to combine several PLT-based enhanced images to obtain the final enhanced image. Finally, a back-lighting image is used to evaluate the performance of the proposed enhancement method and make comparisons with traditional methods.
关键词： power law transform,color image enhancement,CIELAB color space,image fusion
更新于2025-09-04 15:30:14
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Effect of brightness and contrast variation for detectability of root resorption lesions in digital intraoral radiographs
摘要： Objectives To evaluate the performance of periapical radiography assessed under different radiographic brightness and contrast variations in the detection of simulated internal (IRR) and external (ERR) root resorption lesions. Additionally, observers’ preferences related to image quality for these diagnostic tasks were evaluated. Methods Thirty single-root teeth were divided into two groups (n = 15): IRR, in which lesions were simulated using mechanical and biochemical processes; and ERR, in which cavities standardized with drills of different sizes were performed on the root surfaces. Digital radiographs were obtained and subsequently adjusted in 4 additional combinations, resulting in 5 brightness/contrast variations (V1–V5). Five radiologists evaluated the radiographs. The observers’ preference on the image quality was also recorded. Results For both conditions, there were no differences in the accuracy and specificity between the five brightness/contrast variations (p > 0.05), but the sensitivity for ERR was significantly lower in V4 (+ 15% brightness/?15% contrast) in the large size (p < 0.05). The observers classified V2 (? 15% brightness/+15% contrast) as the Bbest^ image quality for IRR and ERR evaluation. Conclusions For IRR and ERR lesions, brightness and contrast variation does not affect the diagnostic performance of digital intraoral radiography within the tested range. The observers prefer images with a reasonable decrease in brightness and increase in contrast. Clinical relevance Brightness and contrast enhancement tools are commonly applied in digital radiographic assessment. The use of these tools for detection of root resorptions can be applied according to the observer preference without influence on diagnostic accuracy.
关键词： Endodontics,Root resorption,Radiographic image enhancement,Digital radiography
更新于2025-09-04 15:30:14
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[Studies in Computational Intelligence] Recent Advances in Computer Vision Volume 804 (Theories and Applications) || Hyperspectral Image: Fundamentals and Advances
摘要： Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more ef?cient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classi?cation techniques and the most recently popular classi?cation algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter.
关键词： image enhancement,restoration,Hyperspectral imaging,classification,remote sensing,denoising,datasets
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) - Cluj-Napoca (2018.9.6-2018.9.8)] 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) - A Deep Learning Approach For Pedestrian Segmentation In Infrared Images
摘要： Semantic segmentation in the context of traffic scenes has been vastly explored using different architectures for deep convolutional networks and color images. In the case of infrared images there is place for improvement and scientific contributions mainly due to the lack of data sets that contain baseline segmentations in the infrared domain. This paper proposes a method for real time infrared pedestrian segmentation using ERFNet. Within the context of the proposed method we study the effect of different basic image enhancement techniques on the performance of the segmentation. We enhance an existing dataset of infrared images with ground truth segmentations for pedestrians. Our experiments show that the proposed method is accurate and appropriate for real time applications.
关键词： pedestrian segmentation,ERFNet,infrared images,deep learning,image enhancement
更新于2025-09-04 15:30:14