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Deep learning models for digital image processing: a review
Within the domain of image processing, a wide array of methodologies is dedicated to tasks
including denoising, enhancement, segmentation, feature extraction, and classification …
including denoising, enhancement, segmentation, feature extraction, and classification …
A review on deep learning in medical image reconstruction
Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most fundamental and …
treatment of diseases. Medical image reconstruction is one of the most fundamental and …
Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network
Infrared and visible image fusion aims to synthesize a single fused image that not only
contains salient targets and abundant texture details but also facilitates high-level vision …
contains salient targets and abundant texture details but also facilitates high-level vision …
An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection
This research focuses on the discovery and localization of hidden objects in the wild and
serves unmanned systems. Through empirical analysis, infrared and visible image fusion …
serves unmanned systems. Through empirical analysis, infrared and visible image fusion …
Blind2unblind: Self-supervised image denoising with visible blind spots
Z Wang, J Liu, G Li, H Han - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile,
supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised …
supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised …
Detection-friendly dehazing: Object detection in real-world hazy scenes
Adverse weather conditions in real-world scenarios lead to performance degradation of
deep learning-based detection models. A well-known method is to use image restoration …
deep learning-based detection models. A well-known method is to use image restoration …
Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising
Mechanical system usually operates in harsh environments, and the monitored vibration
signal faces substantial noise interference, which brings great challenges to the robust fault …
signal faces substantial noise interference, which brings great challenges to the robust fault …
Lg-bpn: Local and global blind-patch network for self-supervised real-world denoising
Despite the significant results on synthetic noise under simplified assumptions, most self-
supervised denoising methods fail under real noise due to the strong spatial noise …
supervised denoising methods fail under real noise due to the strong spatial noise …
Advancing image understanding in poor visibility environments: A collective benchmark study
Existing enhancement methods are empirically expected to help the high-level end
computer vision task: however, that is observed to not always be the case in practice. We …
computer vision task: however, that is observed to not always be the case in practice. We …
Distilling knowledge from super-resolution for efficient remote sensing salient object detection
Current state-of-the-art remote sensing salient object detectors always require high-
resolution spatial context to ensure excellent performance, which incurs enormous …
resolution spatial context to ensure excellent performance, which incurs enormous …