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Found 47 papers, 17 papers with code
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
Multiple Latent Space Mapping for Compressed Dark Image Enhancement
Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space.
SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field
We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output.
Supervised Homography Learning with Realistic Dataset Generation
In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network.
Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions
On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data.
Realistic Noise Synthesis with Diffusion Models
Deep image denoising models often rely on large amount of training data for the high quality performance.
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch
The method combines the efficiency of optical flow and the accuracy of PatchMatch.
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning
Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details.
Deep Homography Mixture for Single Image Rolling Shutter Correction
In this work, we adopt a more straightforward method to learn deep homography mixture motion between an RS image and its corresponding GS image, without large solution space or strict restrictions on image features.
Minimum Latency Deep Online Video Stabilization
In this work, we adopt recent off-the-shelf high-quality deep motion models for motion estimation to recover the camera trajectory and focus on the latter two steps.
Ghost-free High Dynamic Range Imaging with Context-aware Transformer
High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR images with realistic details.
OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement
We design a DCT based convolution layer, to produce DCT coefficients that are suitable for CNN learning.
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal
However, those methods are based on a hypothesis that the value of salt and pepper noise is exactly 0 and 255.
Luminance-Guided Chrominance Image Enhancement for HEVC Intra Coding
In this paper, we propose a luminance-guided chrominance image enhancement convolutional neural network for HEVC intra coding.
Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding
In this paper, we propose a new fast CU partition algorithm for VVC intra coding based on cross-block difference.
Learning To Zoom Inside Camera Imaging Pipeline
To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model.
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization
Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods.
Multi-Decoding Deraining Network and Quasi-Sparsity Based Training
In this work, we aim to exploit the intrinsic priors of rainy images and develop intrinsic loss functions to facilitate training deraining networks, which decompose a rainy image into a rain-free background layer and a rainy layer containing intact rain streaks.
Personalized Outfit Recommendation With Learnable Anchors
To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i. e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes.
FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
Data association is important in the point cloud registration.
Holistic 3D Scene Understanding from a Single Image with Implicit Representation
We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
Ranked #1 on
Monocular 3D Object Detection
on SUN RGB-D
(using extra training data)
OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration
On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features.
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data
The ground truth images play a leading role in generating reasonable HDR images.
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks
We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders.
An analytical theory of CEP-dependent coherence driven by few-cycle pulses
The interaction between an atomic system and a few-cycle ultrafast pulse carries rich physics and a considerable application prospect in quantum-coherence control.
Quantum Physics Optics
Rethinking Image Deraining via Rain Streaks and Vapors
Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light.
Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.
SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping
The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources.
Neural Point Cloud Rendering via Multi-Plane Projection
The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory.
Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning ability
The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks.
Deep Image Deraining Via Intrinsic Rainy Image Priors and Multi-scale Auxiliary Decoding
Different rain models and novel network structures have been proposed to remove rain streaks from single rainy images.
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes
Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network.
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.
Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure
In this paper, we propose a quality enhancement network of versatile video coding (VVC) compressed videos by jointly exploiting spatial details and temporal structure (SDTS).
Rain Removal By Image Quasi-Sparsity Priors
We present a novel rain removal method in this paper, which consists of two steps, i. e., detection of rain streaks and reconstruction of the rain-removed image.
Removing rain streaks by a linear model
Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems.
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image
In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth.
MGANet: A Robust Model for Quality Enhancement of Compressed Video
In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
Differential Evolution with Better and Nearest Option for Function Optimization
Differential evolution(DE) is a conventional algorithm with fast convergence speed.
A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update
In addition, the time complexity of the proposed algorithm is the same as that of MOEA/D, and lower than that of other known MOEAs, since it considers only individuals within the current neighborhood at each update.
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization
However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently.
A mullti- or many- objective evolutionary algorithm with global loop update
The decomposition-based MOEAs emphasize convergence and diversity in a simple model and have made a great success in dealing with theoretical and practical multi- or many-objective optimization problems.
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network
Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene.
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
The problem of low rank matrix completion is considered in this paper.
Whale swarm algorithm for function optimization
Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization.
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors
In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations.
