249 citations · 560 across the 17 of their papers we have counts for
17 papers
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network
Zida Chen, Ziran Zhang, Haoying Li +6
Linear Array Pushbroom (LAP) imaging technology is widely used in the realm of remote sensing. However, images acquired through LAP always suffer from distortion and blur because o…
Revealing the preference for correcting separated aberrations in joint optic-image design
Jingwen Zhou, Shiqi Chen, Zheng Ren +5
The joint design of the optical system and the downstream algorithm is a challenging and promising task. Due to the demand for balancing the global optimal of imaging systems and t…
Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark
Zheyan Jin, Shiqi Chen, Huajun Feng +2
Photographing in the under-illuminated scenes, the presence of complex light sources often leave strong flare artifacts in images, where the intensity, the spectrum, the reflection…
Let Segment Anything Help Image Dehaze
Zheyan Jin, Shiqi Chen, Yueting Chen +2
The large language model and high-level vision model have achieved impressive performance improvements with large datasets and model sizes. However, low-level computer vision tasks…
Adaptive Window Pruning for Efficient Local Motion Deblurring
Haoying Li, Jixin Zhao, Shangchen Zhou +3
Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure. Existing image deblurring methods p…
Computational Optics for Mobile Terminals in Mass Production
Shiqi Chen, Ting Lin, Huajun Feng +3
Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing m…
Optical Aberration Correction in Postprocessing using Imaging Simulation
Shiqi Chen, Huajun Feng, Dexin Pan +3
As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical a…
Reliable Image Dehazing by NeRF
Zheyan Jin, Shiqi Chen, Huajun Feng +3
We present an image dehazing algorithm with high quality, wide application, and no data training or prior needed. We analyze the defects of the original dehazing model, and propose…
Real-World Deep Local Motion Deblurring
Haoying Li, Ziran Zhang, Tingting Jiang +3
Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of l…
Towards Balanced Learning for Instance Recognition
Jiangmiao Pang, Kai Chen, Qi Li +5
Instance recognition is rapidly advanced along with the developments of various deep convolutional neural networks. Compared to the architectures of networks, the training process,…
Class agnostic moving target detection by color and location prediction of moving area
Zhuang He, Qi Li, Huajun Feng +1
Moving target detection plays an important role in computer vision. However, traditional algorithms such as frame difference and optical flow usually suffer from low accuracy or he…
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
Haoying Li, Yifan Yang, Meng Chang +4
Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, which is an ill-posed problem because one LR image cor…
Low-light Image Restoration with Short- and Long-exposure Raw Pairs
Meng Chang, Huajun Feng, Zhihai Xu +1
Low-light imaging with handheld mobile devices is a challenging issue. Limited by the existing models and training data, most existing methods cannot be effectively applied in real…
Beyond Camera Motion Blur Removing: How to Handle Outliers in Deblurring
Meng Chang, Chenwei Yang, Huajun Feng +2
Camera motion deblurring is an important low-level vision task for achieving better imaging quality. When a scene has outliers such as saturated pixels, the captured blurred image…
Spatial-Adaptive Network for Single Image Denoising
Meng Chang, Qi Li, Huajun Feng +1
Previous works have shown that convolutional neural networks can achieve good performance in image denoising tasks. However, limited by the local rigid convolutional operation, the…
Libra R-CNN: Towards Balanced Learning for Object Detection
Jiangmiao Pang, Kai Chen, Jianping Shi +3
Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work…
R-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
Jiangmiao Pang, Cong Li, Jianping Shi +2
Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still rema…