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Image Restoration with Structured Deep Image Prior
2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2021In this study, a novel image restoration method is proposed by introducing a structured convolutional neural network (CNN) in the deep image prior (DIP) framework. CNN has shown significance for image restoration as well as classification. DIP uses CNN structures as an image prior and shows a significant performance without explicit training of the ...
Jikai Li +3 more
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Enhanced PET imaging using progressive conditional deep image prior
Physics in Medicine & Biology, 2023Abstract Objective. Unsupervised learning-based methods have been proven to be an effective way to improve the image quality of positron emission tomography (PET) images when a large dataset is not available. However, when the gap between the input image and the target PET image is large, direct unsupervised learning ...
Jinming Li +6 more
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Deep CNN Prior Based Image Reconstruction for Multispectral Imaging
2020 28th Signal Processing and Communications Applications Conference (SIU), 2020Spectral imaging is a widely used diagnostic technique in various fields such as physics, chemistry, biology, medicine, astronomy, and remote sensing. In this work, we focus on a multi-spectral imaging technique with a diffractive lens, which relies on computational imaging, and we develop a novel image reconstruction method that exploits convolutional
Manisali, İrfan +3 more
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Learning Deep Priors for Image Dehazing
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019Image dehazing is a well-known ill-posed problem, which usually requires some image priors to make the problem well-posed. We propose an effective iteration algorithm with deep CNNs to learn haze-relevant priors for image dehazing. We formulate the image dehazing problem as the minimization of a variational model with favorable data fidelity terms and ...
Yang Liu +3 more
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Deep Gaussian Scale Mixture Prior for Image Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Image reconstruction from partial observations has attracted increasing attention. Conventional image reconstruction methods with hand-crafted priors often fail to recover fine image details due to the poor representation capability of the hand-crafted priors.
Tao Huang +4 more
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Deep Random Projector: Accelerated Deep Image Prior
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023Taihui Li +3 more
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Structure-Texture Image Decomposition Using Deep Variational Priors
IEEE Transactions on Image Processing, 2019Most variational formulations for structure-texture image decomposition force structure images to have small norm in some functional spaces, and share a common notion of edges, i.e., large-gradients or -intensity differences. However, such definition makes it difficult to distinguish structure edges from oscillations that have fine spatial scale but ...
Youngjung Kim +3 more
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Deep image prior for polarization image demosaicking
2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 2023Yinxia Shi, Desheng Wen, Tuochi Jiang
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Boosting deep image prior by integrating external and internal image priors
Journal of Electronic Imaging, 2023Shaoping Xu +4 more
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Deep Image Denoising With Adaptive Priors
IEEE Transactions on Circuits and Systems for Video Technology, 2022Bo Jiang +4 more
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