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Register a new userA Comprehensive Survey on Image Dehazing Based on Deep Learning
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Added by
Jie Gui
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed dehazing methods. Firstly, we summarize the commonly used datasets, loss functions and evaluation metrics. Secondly, we group the existing researches of ID into two major categories: supervised ID and unsupervised ID. The core ideas of various influential dehazing models are introduced. Finally, the open issues for future research on ID are pointed out.
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Recently it has shown that the policy-gradient methods for reinforcement learning have been utilized to train deep end-to-end systems on natural language processing tasks. Whats more, with the complexity of understanding image content and diverse ways of describing image content in natural language, image captioning has been a challenging problem to deal with. To the best of our knowledge, most state-of-the-art methods follow a pattern of sequential model, such as recurrent neural networks (RNN). However, in this paper, we propose a novel architecture for image captioning with deep reinforcement learning to optimize image captioning tasks. We utilize two networks called policy network and value network to collaboratively generate the captions of images. The experiments are conducted on Microsoft COCO dataset, and the experimental results have verified the effectiveness of the proposed method.
As a common image editing operation, image composition aims to cut the foreground from one image and paste it on another image, resulting in a composite image. However, there are many issues that could make the composite images unrealistic. These issues can be summarized as the inconsistency between foreground and background, which include appearance inconsistency (e.g., incompatible color and illumination) and geometry inconsistency (e.g., unreasonable size and location). Previous works on image composition target at one or more issues. Since each individual issue is a complicated problem, there are some research directions (e.g., image harmonization, object placement) which focus on only one issue. By putting all the efforts together, we can acquire realistic composite images. Sometimes, we expect the composite images to be not only realistic but also aesthetic, in which case aesthetic evaluation needs to be considered. In this survey, we summarize the datasets and methods for the above research directions. We also discuss the limitations and potential directions to facilitate the future research for image composition. Finally, as a double-edged sword, image composition may also have negative effect on our lives (e.g., fake news) and thus it is imperative to develop algorithms to fight against composite images. Datasets and codes for image composition are summarized at https://github.com/bcmi/Awesome-Image-Composition.
Schema-based event extraction is a critical technique to apprehend the essential content of events promptly. With the rapid development of deep learning technology, event extraction technology based on deep learning has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models. We summarize the task definition, paradigm, and models of schema-based event extraction and then discuss each of these in detail. We introduce benchmark datasets that support tests of predictions and evaluation metrics. A comprehensive comparison between different techniques is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.
Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems.
The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community. Several DL compilers have been proposed from both industry and academia such as Tensorflow XLA and TVM. Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardware as output. However, none of the existing survey has analyzed the unique design architecture of the DL compilers comprehensively. In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations. Specifically, we provide a comprehensive comparison among existing DL compilers from various aspects. In addition, we present detailed analysis on the design of multi-level IRs and illustrate the commonly adopted optimization techniques. Finally, several insights are highlighted as the potential research directions of DL compiler. This is the first survey paper focusing on the design architecture of DL compilers, which we hope can pave the road for future research towards DL compiler.
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