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Found 11 papers, 6 papers with code
Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising
However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising.
Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network
Recently, the transformation of standard dynamic range TV (SDRTV) to high dynamic range TV (HDRTV) is in high demand due to the scarcity of HDRTV content.
FSI: Frequency and Spatial Interactive Learning for Image Restoration in Under-Display Cameras
In this paper, we introduce a new perspective to handle various diffraction in UDC images by jointly exploring the feature restoration in the frequency and spatial domains, and present a Frequency and Spatial Interactive Learning Network (FSI).
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling
Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream.
Ranked #1 on
Image Denoising
on SID SonyA7S2 x100
Robust Deep Ensemble Method for Real-world Image Denoising
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Ranked #1 on
Image Denoising
on SIDD
(SSIM (sRGB) metric)
Practical Deep Raw Image Denoising on Mobile Devices
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks.
A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs).
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on
Scene Text Detection
on COCO-Text
Curved Text Detection
Optical Character Recognition (OCR)
+1
