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Found 28 papers, 10 papers with code
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images.
An Empirical Study of GPT-4o Image Generation Capabilities
The landscape of image generation has rapidly evolved, from early GAN-based approaches to diffusion models and, most recently, to unified generative architectures that seek to bridge understanding and generation tasks.
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model
Recent advancements in vision-language models (VLMs) for common-sense reasoning have led to the development of vision-language-action (VLA) models, enabling robots to perform generalized manipulation.
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
To the best of our knowledge, RoboMIND is the largest multi-embodiment teleoperation dataset collected on a unified platform, providing large-scale and high-quality robotic training data.
Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation
Based on the mapping, Lift3D utilizes the 2D foundation model to directly encode point cloud data, leveraging large-scale pretrained knowledge to construct explicit 3D robotic representations while minimizing spatial information loss.
Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization
Specifically, we construct a real-world dataset with 85 snowy videos, and then present a Semi-supervised Video Desnowing Network (SemiVDN) equipped by a novel Distribution-driven Contrastive Regularization.
Ranked #2 on
Snow Removal
on RVSD
(using extra training data)
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression.
Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint
Recent advancements in adverse weather restoration have shown potential, yet the unpredictable and varied combinations of weather degradations in the real world pose significant challenges.
Cross-conditioned Diffusion Model for Medical Image to Image Translation
First, we propose a Modality-specific Representation Model (MRM) to model the distribution of target modalities.
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models
RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration.
AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement
Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications.
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation
Robot manipulation policies have shown unsatisfactory action performance when confronted with novel task or object instances.
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.
Learning Diffusion Texture Priors for Image Restoration
When adopting diffusion models for image restoration the crucial challenge lies in how to preserve high-level image fidelity in the randomness diffusion process and generate accurate background structures and realistic texture details.
VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook
In this paper, we propose Clearer Night Image Restoration with Vector-Quantized Codebook (VQCNIR) to achieve remarkable and consistent restoration outcomes on real-world and synthetic benchmarks.
Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image.
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficient and lightweight real-time underwater image enhancement network with only $\sim$ 9k parameters and $\sim$ 0. 01s processing time.
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges.
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles.
Adverse Weather Removal with Codebook Priors
Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal.
Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration
Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs.
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing
Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task.
Ranked #2 on
Single Image Desnowing
on CSD
Towards Real-time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-decoder Architecture
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image.
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing
Snow removal causes challenges due to its characteristic of complex degradations.
Towards Efficient Single Image Dehazing and Desnowing
A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compact architecture and three novel components.
Underwater Light Field Retention : Neural Rendering for Underwater Imaging
To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering).
Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle
Specifically, we first devise two siamese networks: a teacher network in the synthetic domain and a student network in the real domain, and then optimize them in a mutual learning manner by leveraging EMA and joint loss.
