1,224 research outputs found
A survey of DNN methods for blind image quality assessment
Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Low-Dose CT Image Enhancement Using Deep Learning
The application of ionizing radiation for diagnostic imaging is common around
the globe. However, the process of imaging, itself, remains to be a relatively
hazardous operation. Therefore, it is preferable to use as low a dose of
ionizing radiation as possible, particularly in computed tomography (CT)
imaging systems, where multiple x-ray operations are performed for the
reconstruction of slices of body tissues. A popular method for radiation dose
reduction in CT imaging is known as the quarter-dose technique, which reduces
the x-ray dose but can cause a loss of image sharpness. Since CT image
reconstruction from directional x-rays is a nonlinear process, it is
analytically difficult to correct the effect of dose reduction on image
quality. Recent and popular deep-learning approaches provide an intriguing
possibility of image enhancement for low-dose artifacts. Some recent works
propose combinations of multiple deep-learning and classical methods for this
purpose, which over-complicate the process. However, it is observed here that
the straight utilization of the well-known U-NET provides very successful
results for the correction of low-dose artifacts. Blind tests with actual
radiologists reveal that the U-NET enhanced quarter-dose CT images not only
provide an immense visual improvement over the low-dose versions, but also
become diagnostically preferable images, even when compared to their full-dose
CT versions
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Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 2
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
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