Multi-focused plenoptic images possess many special characteristics related to the micro-images (MIs) array, which are expected to be useful in further increasing its compression performance. Those special characteris...
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Learning-based image compression methods have emerged as state-of-The-Art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a ne...
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The multi-exposure fusion algorithms can effectively improve the dynamic range of light field imaging, but distortion is inevitably introduced during the process. Therefore, it is crucial to construct an effective qua...
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In the Internet era, the explosive growth of media data processing poses significant challenges for the research of image Coding for Machines (ICM) in improving the efficiency of AI models while reducing the burdens o...
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Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artifi...
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ISBN:
(纸本)9781665475921
Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achieve satisfied retrieval performance. In this paper, a new end-to-end encrypted image retrieval scheme is presented. First, images are encrypted by using block rotation, new orthogonal transforms and block permutation during the JPEG compression process. Second, we combine the triplet loss and the cross entropy loss to train a network model, which contains gMLP modules, by end-to-end learning for extracting cipher-images' features. Compared with manual features extraction such as extracting color histogram, the end-to-end mechanism can economize on manpower. Experimental results show that our scheme has good retrieval performance, while can ensure compression friendly and no feature leakage.
With the increasing popularity of commercial depth cameras, 3D reconstruction of dynamic scenes has aroused widespread interest. Although many novel 3D applications have been unlocked, real-time performance is still a...
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ISBN:
(纸本)9781665475921
With the increasing popularity of commercial depth cameras, 3D reconstruction of dynamic scenes has aroused widespread interest. Although many novel 3D applications have been unlocked, real-time performance is still a big problem. In this paper, a low-cost, real-time system: LiveRecon3D, is presented, with multiple RGB-D cameras connected to one single computer. The goal of the system is to provide an interactive frame rate for 3D content capture and rendering at a reduced cost. In the proposed system, we adopt a scalable volume structure and employ ray casting technique to extract the surface of 3D content. Based on a pipeline design, all the modules in the system run in parallel and are designed to minimize the latency to achieve an interactive frame rate of 30 FPS. At last, experimental results corresponding to implementation with three Kinect v2 cameras are presented to verify the system's effectiveness in terms of visual quality and real-time performance.
The human visual system finds salient regions in images and allows the cognitive ability to focus on them. Hence, such salient regions play substantial roles in determining the quality of images. However, the existing...
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ISBN:
(纸本)9781728198354
The human visual system finds salient regions in images and allows the cognitive ability to focus on them. Hence, such salient regions play substantial roles in determining the quality of images. However, the existing super-resolution (SR) methods restore all regions of low-resolution images in the same manner. In this paper, we first propose a saliency-guided image super-resolution (SGSR) network where its restoration ability concentrates on the salient regions in the natural images. For this, we propose a saliency learning scheme using newly computed saliency scores for object regions. Then, by providing the saliency features to the saliency-guided attention (SGA) module and using a novel saliency-weighted loss function, the SGSR maximizes the image quality of salient regions and suppresses the excessive generation of unnecessary structures in backgrounds. To the best of our knowledge, this SGSR is the first attempt to induce discriminatory results guided by saliency in the field of natural image SR.
Creating hyper-realistic synthetic images has become effortless with tremendous development in Generative Artificial Intelligence technologies. Generative Adversarial Networks (GAN) generated synthetic images, especia...
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image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, objec...
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ISBN:
(纸本)9781665475921
image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.
Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model tha...
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