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Found 23 papers, 6 papers with code
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low dynamic range images.
Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules.
Generating Content for HDR Deghosting from Frequency View
To address this challenge, we propose the Low-Frequency aware Diffusion (LF-Diff) model for ghost-free HDR imaging.
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters.
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space.
Ranked #1 on
Low-Light Image Enhancement
on VV
Low-light Image Deblurring and Enhancement
Low-Light Image Enhancement
Multiple Object Tracking based on Occlusion-Aware Embedding Consistency Learning
The OPM predicts occlusion information for each true detection, facilitating the selection of valid samples for consistency learning of the track's visual embedding.
Towards High-quality HDR Deghosting with Conditional Diffusion Models
To address this challenge, we formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition, consisting of the feature condition generator and the noise predictor.
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task.
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation
To address this issue, we propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can effectively capture and utilize accurate degradation representation for image restoration.
A Unified HDR Imaging Method with Pixel and Patch Level
The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork.
SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders
In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR.
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features.
Take a Prior from Other Tasks for Severe Blur Removal
For general scene deblurring, the feature space of the blurry image and corresponding sharp image under the high-level vision task is closer, which inspires us to rely on other tasks (e. g. classification) to learn a comprehensive prior in severe blur removal cases.
Stock Market Prediction via Deep Learning Techniques: A Survey
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes
In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP).
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning
This minimizes the interference between parameters for different tasks.
Ranked #5 on
Continual Learning
on Tiny-ImageNet (10tasks)
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree.
Attention-based network for low-light image enhancement
Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.
COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.
3D Gated Recurrent Fusion for Semantic Scene Completion
This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion.
Ranked #14 on
3D Semantic Scene Completion
on NYUv2
Attention-guided Network for Ghost-free High Dynamic Range Imaging
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes.
