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Found 15 papers, 2 papers with code
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors
Our KLAP and KLAP-M methods achieved state-of-the-art demosaicing performance in both synthetic and real RAW data of Bayer and non-Bayer CFAs.
All-in-One Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations
Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Coil2Coil: Self-supervised MR image denoising using phased-array coil images
Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Blur More To Deblur Better: Multi-Blur2Deblur For Efficient Video Deblurring
Secondly, we propose multi-blurring recurrent neural network (MBRNN) that can synthesize more blurred images from neighboring frames, yielding substantially improved performance with existing video deblurring methods.
Ranked #5 on
Deblurring
on DVD
(using extra training data)
Task-Aware Variational Adversarial Active Learning
Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques.
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training
Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales.
Ranked #14 on
Deblurring
on HIDE (trained on GOPRO)
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection
Recently, robotic grasp detection (GD) and object detection (OD) with reasoning have been investigated using deep neural networks (DNNs).
Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring
Our proposed CNN-based down-scaling was the key factor for this excellent performance since the performance of our network without it was decreased by 1. 98dB.
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module
However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects.
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images
Our methods also achieved state-of-the-art detection accuracy (up to 96. 6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset.
Classification based Grasp Detection using Spatial Transformer Network
Typically, regression based grasp detection methods have outperformed classification based detection methods in computation complexity with excellent accuracy.
