Towards a Video Quality Assessment based Framework for Enhancement of Laparoscopic Videos
Zohaib Amjad Khan, Azeddine Beghdadi, Faouzi Alaya Cheikh, Mounir Kaaniche, Egidijus Pelanis, Rafael Palomar, Åsmund Avdem Fretland, Bjørn Edwin, Ole Jakob Elle
备注：
SPIE Medical Imaging 2020 (Draft version)
发表信息：
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160P (2020)
Laparoscopic videos can be affected by different distortions which may impact the performance of surgery and introduce surgical errors. In this work, we propose a framework for automatically detecting and identifying such distortions and their severity using video quality assessment. There are three major contributions presented in this work (i) a proposal for a novel video enhancement framework for laparoscopic surgery; (ii) a publicly available database for quality assessment of laparoscopic videos evaluated by expert as well as non-expert observers and (iii) objective video quality assessment of laparoscopic videos including their correlations with expert and non-expert scores.
Norwegian University of Science and Technology (NTNU)（挪威科技大学）
Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures wh…
Université Sorbonne Paris Nord（索邦巴黎北大学）；Institut Galilée（伽利略研究所）；NTNU（挪威科技大学）
Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking a…
One-shot skill assessment in high-stakes domains with limited data via meta learning
Florida A&M University and The Florida State University（佛罗里达农工大学与佛罗里达州立大学）；University at Buffalo（布法罗大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；U.S. Army Combat Capabilities Development Command Soldier Center STTC（美国陆军作战能力发展司令部士兵中心 STTC）
Recently, many video enhancement methods have been proposed to improve video quality from different aspects such as color, brightness, contrast, and stability. Therefore, how to evaluate the quality of the enhanced video in a way consistent with human visual perception is an important research topic. However, most video quality assessment methods mainly calculate video quality by estimating the distortion degrees of videos from an overall perspective. Few researchers have specifically proposed a video quality assessment method for video enhancement, and there is also no comprehensive video qua…
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；Fondazione Policlinico Universitario A. Gemelli IRCCS（杰梅利大学医院基金会）；IHU-Strasbourg, Institute of Image-Guided Surgery（斯特拉斯堡大学医院-图像引导手术研究所）；Institute for Research against Digestive Cancer (IRCAD)（消化癌症研究所）
Minimally invasive image-guided surgery heavily relies on vision. Deep learning models for surgical video analysis could therefore support visual tasks such as assessing the critical view of safety (CVS) in laparoscopic cholecystectomy (LC), potentially contributing to surgical safety and efficiency. However, the performance, reliability and reproducibility of such models are deeply dependent on the quality of data and annotations used in their development. Here, we present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria. We…
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
Department of Computer Science, South China Normal University（华南师范大学计算机科学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
City University of Hong Kong（香港城市大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhancement. Conventional subjective testing requires manually pre-selecting a small set of visual examples, which may suffer from three sources of biases: 1) sampling bias due to the extremely sparse distribution of the selected samples in the image space; 2) algorithmic bias due to p…
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；PAII Inc（PAII公司）
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-…
Fundus Image Quality Assessment and Enhancement: a Systematic Review
Research Institute of Trustworthy Autonomous Systems, SUSTech（南方科技大学可信自主系统研究院）；Department of Computer Science and Engineering, SUSTech（南方科技大学计算机科学与工程系）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Computer School, Beijing Information Science and Technology University（北京信息科技大学计算机学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
Recently, image quality assessment (IQA) has achieved remarkable progress with the success of deep learning. However, the strict pre-condition of full-reference (FR) methods has limited its application in real scenarios. And the no-reference (NR) scheme is also inconvenient due to its unsatisfying performance as a result of ignoring the essence of image quality. In this paper, we introduce a brand new scheme, namely external-reference image quality assessment (ER-IQA), by introducing external reference images to bridge the gap between FR and NR-IQA. As the first implementation and a new baseli…
Sliced Maximal Information Coefficient: A Training-Free Approach for Image Quality Assessment Enhancement
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；School Of Computer Science, South China Normal University（华南师范大学计算机学院）
Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, such as 2-D classification, removing uninformative images, constructing {ab initio} models, generating templates for particle picking, providing a quick assessment of the data set, dimensionality reduction, and symmetry detection. The algorithm includes built-in quality measures to…
EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment
State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University（信息服务网络国家重点实验室，电子工程学院，西安电子科技大学）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（协同智能系统重点实验室，教育部，西安电子科技大学）；College of Mathematical Science, Inner Mongolia Normal University（数学科学学院，内蒙古师范大学）；School of Electronic Engineering, Xidian University（电子工程学院，西安电子科技大学）
School of Electrical Engineering, Korea University（高丽大学电气工程学院）；LG Display（LG显示公司）
Various power-constrained contrast enhancement (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display while preserving the image quality. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast o…
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challeng…
This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively and quantitatively using Image Quality Assessment (IQA) metrics, and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures cou…
University of Würzburg（维尔茨堡大学）；Institute of Computer Science（计算机科学研究所）
Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may get a lower perceptual quality score using traditional perceptual quality metrics such as PSNR or SSIM. Therefore, it is necessary to develop a quantitative metric to reflect the performance of new algorithms, which should be well-aligned with the person's mean opinion score (MOS). Learning-based approaches for perceptual image quality assessment (IQA) usua…
Qingdao University（青岛大学）；Ocean University of China（中国海洋大学）
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of comprehensive human subjective user study with publicly available dataset and reliable objective UIQA metric. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sour…
This paper proposes a biologically-inspired low-level spatiochromatic-model-based similarity method (BLeSS) to assist full-reference image-quality estimators that originally oversimplify color perception processes. More specifically, the spatiochromatic model is based on spatial frequency, spatial orientation, and surround contrast effects. The assistant similarity method is used to complement image-quality estimators based on phase congruency, gradient magnitude, and spectral residual. The effectiveness of BLeSS is validated using FSIM, FSIMc and SR-SIM methods on LIVE, Multiply Distorted LIV…
In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. Existing multi-method studies focus on proposing a single quality estimator. On the contrary, we investigate the generalizability of multi-method fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion, we propose using neural networks in the boosting. To span different types of image quality assessment algorithms, we use quality estimators based on fidelity, perceptually-extended fidelity, structural similarity, spectral similarity, …
