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DiffLight: Integrating Content and Detail for Low-light Image Enhancement
Yixu Feng;Shuo Hou;Haotian Lin;Yu Zhu;Peng Wu;Wei Dong;Jinqiu Sun;Qingsen Yan;Yanning Zhang
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition, resulting in significant noise black pseudo artifacts in images, which not only degrade visual quality but also affect the performance of down-stream visual tasks. However, current methods often produ...Show More
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
Axi Niu;Trung X. Pham;Kang Zhang;Jinqiu Sun;Yu Zhu;Qingsen Yan;In So Kweon;Yanning Zhang
IEEE Transactions on Broadcasting
Cited by: Papers (5)
Diffusion models have gained significant popularity for image-to-image translation tasks. Previous efforts applying diffusion models to image super-resolution have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinem...Show More
Diffevent: Event Residual Diffusion for Image Deblurring
Pei Wang;Jiumei He;Qingsen Yan;Yu Zhu;Jinqiu Sun;Yanning Zhang
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Traditional frame-based cameras inevitably suffer from non-uniform blur in real-world scenarios. Event cameras that record the intensity changes with high temporal resolution provide an effective solution for image deblurring. In this paper, we formulate the event-based image deblurring as an image generation problem by designing diffusion priors for the image and residual. Specifically, we propos...Show More
SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders
Qingsen Yan;Song Zhang;Weiye Chen;Hao Tang;Yu Zhu;Jinqiu Sun;Luc Van Gool;Yanning Zhang
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data. However, it is difficult for modern DNN...Show More
A Unified HDR Imaging Method with Pixel and Patch Level
Qingsen Yan;Weiye Chen;Song Zhang;Yu Zhu;Jinqiu Sun;Yanning Zhang
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (21)
Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To ge...Show More
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