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Found 13 papers, 10 papers with code
A Decoupled Learning Scheme for Real-world Burst Denoising from Raw Images
On one hand, most of the models are trained on video sequences with synthetic noise.
Event-Based Motion Magnification
Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications.
Toward Accurate and Temporally Consistent Video Restoration from Raw Data
Extensive experiments demonstrate the leading VJDD performance of our method in term of restoration accuracy, perceptual quality and temporal consistency.
TMP: Temporal Motion Propagation for Online Video Super-Resolution
Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information, while the strict latency requirement makes accurate and efficient alignment very challenging.
Spatial-Frequency Attention for Image Denoising
In this paper, we propose the spatial-frequency attention network (SFANet) to enhance the network's ability in exploiting long-range dependency.
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift
Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data.
Efficient Long-Range Attention Network for Image Super-resolution
A highly efficient long-range attention block (ELAB) is then built by simply cascading two shift-conv with a GMSA module, which is further accelerated by using a shared attention mechanism.
Ranked #11 on
Image Super-Resolution
on Manga109 - 4x upscaling
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions.
Text Prior Guided Scene Text Image Super-resolution
Our experiments on the benchmark TextZoom dataset show that TPGSR can not only effectively improve the visual quality of scene text images, but also significantly improve the text recognition accuracy over existing STISR methods.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset
Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images
Considering the fact that the green channel has twice the sampling rate and better quality than the red and blue channels in CFA raw data, we propose to use this green channel prior (GCP) to build a GCP-Net for the JDD-B task.
Toward Convolutional Blind Denoising of Real Photographs
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Ranked #4 on
Denoising
on Darmstadt Noise Dataset
Learning Dynamic Guidance for Depth Image Enhancement
To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance.
