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Found 19 papers, 7 papers with code
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network
Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge.
Revealing the preference for correcting separated aberrations in joint optic-image design
The joint design of the optical system and the downstream algorithm is a challenging and promising task.
Let Segment Anything Help Image Dehaze
Therefore, we propose a framework to integrate large-model prior into low-level computer vision tasks.
Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark
The procedure is comprehensive, where the similarity of scattered flares and the symmetric effect of reflected ghosts are realized.
Adaptive Window Pruning for Efficient Local Motion Deblurring
Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecessary computation on sharp pixels, especially for high-resolution images.
Optical Aberration Correction in Postprocessing using Imaging Simulation
Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems.
Computational Optics for Mobile Terminals in Mass Production
Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task.
Reliable Image Dehazing by NeRF
In order to obtain real shot data in different scenes, we used fog generators, array cameras, mobile phones, underwater cameras and drones to obtain haze data.
Non-blind optical degradation correction via frequency self-adaptive and finetune tactics
In mobile photography applications, limited volume constraints the diversity of optical design.
Real-World Deep Local Motion Deblurring
Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements.
Towards Balanced Learning for Instance Recognition
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
Class agnostic moving target detection by color and location prediction of moving area
Then, the color and location probability map of the moving area will be calculated through maximum a posteriori probability.
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
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 corresponds to multiple HR images.
Extreme-Quality Computational Imaging via Degradation Framework
To meet the space limitation of optical elements, free-form surfaces or high-order aspherical lenses are adopted in mobile cameras to compress volume.
Low-light Image Restoration with Short- and Long-exposure Raw Pairs
Using our data generation method and the proposed LSFNet, we can recover the details and color of the original scene, and improve the low-light image quality effectively.
Beyond Camera Motion Blur Removing: How to Handle Outliers in Deblurring
In this paper, we propose a novel method to handle camera motion blur with outliers.
Spatial-Adaptive Network for Single Image Denoising
An encoder-decoder structure with a context block is introduced to capture multiscale information.
Ranked #7 on
Image Denoising
on DND
(using extra training data)
Libra R-CNN: Towards Balanced Learning for Object Detection
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
Ranked #149 on
Object Detection
on COCO test-dev
R$^2$-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network ($\mathcal{R}^2$-CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector.
