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.
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Ranked #3 on Image Denoising on DND
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
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.
Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs).
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on Scene Text Detection on COCO-Text