Results 51 to 60 of about 1,132,388 (176)
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
, 2017 Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images.
Chen, Yu +4 more
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Image deblurring method driven by double layer convolution neural network denoising module
Nantong Daxue xuebao. Ziran kexue ban, 2022 To solve this problem for inflexible of noise levels for deep convolution neural network for image denoising, an image deblurring method driven by a double deep convolution neural network for image denoising is proposed.The learning capability of ...
WU Jingjing; MA Jingning; ZHU Yonggui
doaj +1 more source
A Model-Driven Deep Dehazing Approach by Learning Deep Priors
IEEE Access, 2021 Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks.
Dong Yang, Jian Sun
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Unsupervised Lesion Detection via Image Restoration with a Normative Prior
, 2019 Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples.
Chen, Xiaoran +3 more
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A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model
Applied SciencesCompared to supervised denoising models based on deep learning, the unsupervised Deep Image Prior (DIP) denoising approach offers greater flexibility and practicality by operating solely with the given noisy image.
Shuichen Ji +5 more
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Deep Graph Laplacian Regularization for Robust Denoising of Real Images
, 2019 Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise.
Cheung, Gene +3 more
core +1 more source
2024 IEEE International Conference on Computational Photography (ICCP)Phase-coded imaging is a computational imaging method designed to tackle tasks such as passive depth estimation and extended depth of field (EDOF) using depth cues inserted during image capture. Most of the current deep learning-based methods for depth estimation or all-in-focus imaging require a training dataset with high-quality depth maps and an ...
Shabtay, Nimrod +2 more
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Early Stopping for Deep Image Prior
, 2021 Deep image prior (DIP) and its variants have showed remarkable potential for solving inverse problems in computer vision, without any extra training data. Practical DIP models are often substantially overparameterized. During the fitting process, these models learn mostly the desired visual content first, and then pick up the potential modeling and ...
Wang, Hengkang +5 more
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Learning Deep Image Priors for Blind Image Denoising [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019 Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realistic domain. In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment.
Hou, Xianxu +7 more
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Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
, 2018 In recent years, various shadow detection methods from a single image have been proposed and used in vision systems; however, most of them are not appropriate for the robotic applications due to the expensive time complexity. This paper introduces a fast
Hosseinzadeh, Sepideh +2 more
core +1 more source