Search Results for author:
Found 34 papers, 13 papers with code
OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation
Current scene reconstruction techniques frequently result in the loss of object detail textures and are unable to reconstruct object portions that are occluded or unseen in views.
SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream
Spike cameras, leveraging spike-based integration sampling and high temporal resolution, offer distinct advantages over standard cameras.
Physics-Inspired Degradation Models for Hyperspectral Image Fusion
To fill this gap, we propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI, which comprises a spatial degradation network (SpaDN) and a spectral degradation network (SpeDN).
In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging
Dual-Camera Compressed Hyperspectral Imaging (DCCHI) offers the capability to reconstruct 3D Hyperspectral Image (HSI) by fusing compressive and Panchromatic (PAN) image, which has shown great potential for snapshot hyperspectral imaging in practice.
Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond
Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations.
Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence
By integrating unified spatial-spectral attention and linear dependence, our ECT can model exhaustive correlation within HSI.
ShareCMP: Polarization-Aware RGB-P Semantic Segmentation
Multimodal semantic segmentation is developing rapidly, but the modality of RGB-Polarization remains underexplored.
Ranked #1 on
Semantic Segmentation
on ZJU-RGB-P
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising
Specifically, we integrate physical priors into neural proxies and introduce three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution loss (DDL).
A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection
This dataset included details on 5, 838 distinct genotypes and daily weather data for a 214-day growing season, enabling comprehensive analysis.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Mutual-Guided Dynamic Network for Image Fusion
Image fusion aims to generate a high-quality image from multiple images captured under varying conditions.
Recurrent Spike-based Image Restoration under General Illumination
Spike camera is a new type of bio-inspired vision sensor that records light intensity in the form of a spike array with high temporal resolution (20, 000 Hz).
Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding.
Risk-averse Stochastic Optimization for Farm Management Practices and Cultivar Selection Under Uncertainty
Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint.
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling
Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream.
Ranked #1 on
Image Denoising
on SID SonyA7S2 x100
Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units
In the 2021 Syngenta crop challenge in analytics, Syngenta raised the problem to design an optimization model for the planting time scheduling in the 2020 year-round breeding process so that there is a consistent harvest quantity each week.
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging
In this paper, we propose the quantization-aware deep optics for diffractive snapshot hyperspectral imaging.
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation
In this paper, we propose Neural Spectral Reconstruction (NeSR) to lift this limitation, by introducing a novel continuous spectral representation.
A reinforcement learning approach to resource allocation in genomic selection
Genomic selection (GS) is a technique that plant breeders use to select individuals to mate and produce new generations of species.
Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction
Snapshot hyperspectral imaging has been developed to capture the spectral information of dynamic scenes.
WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting
To help mitigate this data collection bottleneck in wheat breeding, we propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making.
Snapshot Hyperspectral Imaging Based on Weighted High-order Singular Value Regularization
In this paper, we propose an effective high-order tensor optimization based method to boost the reconstruction fidelity for snapshot hyperspectral imaging.
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning
A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields.
High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks
The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society.
DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation
In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield.
DNU: Deep Non-Local Unrolling for Computational Spectral Imaging
In this paper, we propose an interpretable neural network for computational spectral imaging.
Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
The sliding window approach uses a convolutional neural network (CNN) for kernel detection.
Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
In the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical yield performances of around 4% of total cross combinations of 593 inbreds with 496 testers which were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical yield data collected from crossing other inbreds and testers.
A CNN-RNN Framework for Crop Yield Prediction
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions.
Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture.
Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior
In this paper, we present a novel hyperspectral image reconstruction algorithm that substitutes the traditional hand-crafted prior with a data-driven prior, based on an optimization-inspired network.
Crop Yield Prediction Using Deep Neural Networks
Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions.
Ranked #1 on
Crop Yield Prediction
on 2018 Syngenta (2016 val)
Snapshot Hyperspectral Light Field Imaging
This paper presents the first snapshot hyperspectral light field imager in practice.
High-Speed Hyperspectral Video Acquisition With a Dual-Camera Architecture
We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution.
