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Found 20 papers, 9 papers with code
Physics-based Feature Dehazing Networks
We propose an effective feature dehazing unit (FDU), which is applied to the deep feature space to explore useful features for image dehazing based on the physics model.
Ranked #20 on
Image Dehazing
on SOTS Indoor
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization
To explore this property for better spatial and temporal feature utilization, we develop a local attention module to aggregate the features from adjacent frames in a spatial-temporal neighborhood.
Collaborative Feedback Discriminative Propagation for Video Super-Resolution
However, inaccurate alignment usually leads to aligned features with significant artifacts, which will be accumulated during propagation and thus affect video restoration.
Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining
To better explore the common degradation representations from spatially-varying rain streaks, we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design, enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios.
Towards Unified Deep Image Deraining: A Survey and A New Benchmark
In this paper, we provide a comprehensive review of existing image deraining method and provide a unify evaluation setting to evaluate the performance of image deraining methods.
LUT-GCE: Lookup Table Global Curve Estimation for Fast Low-light Image Enhancement
To further speed up the inference speed, a lookup table method is employed for fast retrieval.
SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency
For this purpose, we develop a prompt based on the features of depth differences between the hazy input images and corresponding clear counterparts that can guide dehazing models for better restoration.
Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution
Although numerous solutions have been proposed for image super-resolution, they are usually incompatible with low-power devices with many computational and memory constraints.
Ranked #38 on
Image Super-Resolution
on Set14 - 4x upscaling
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution
We develop a hybrid dynamic-Transformer block(HDTB) that integrates the MHDLSA and SparseGSA for both local and global feature exploration.
Multi-Scale Residual Low-Pass Filter Network for Image Deblurring
We present a simple and effective Multi-scale Residual Low-Pass Filter Network (MRLPFNet) that jointly explores the image details and main structures for image deblurring.
Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring
In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring.
Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring
We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring.
Ranked #1 on
Image Deblurring
on GoPro
(using extra training data)
Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring
The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization.
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning.
Learning Data Terms for Non-blind Deblurring
We propose a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers.
Physics-Based Generative Adversarial Models for Image Restoration and Beyond
We present an algorithm to directly solve numerous image restoration problems (e. g., image deblurring, image dehazing, image deraining, etc.).
Learning Discriminative Data Fitting Functions for Blind Image Deblurring
Solving blind image deblurring usually requires defining a data fitting function and image priors.
Blind Image Deblurring With Outlier Handling
We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally.
