In this paper, we propose an Electrocardiogram (ECG) classification model based on FFC (Fast Fourier Convolution) and ResNet. The model utilizes FFC and ResNet for feature extraction and classification. We further improve the network performance and convergence speed through batch normalization and residual concatenation. The experimental results demonstrate that the model exhibits excellent classification performance under different data distributions in the PTB-XL database and trains faster than traditional ResNet models. Additionally, we introduce a new module, FFC-R, and validate its excellent performance in ECG classification tasks. This innovation is expected to provide powerful support for the diagnosis and treatment of heart diseases.
Old photographs often suffer from a variety of imperfections, such as scratches, stains, and fading. Various techniques have been proposed for image restoration, including image inpainting, denoising, deblurring, and super-resolution. In this paper, we propose a novel approach to restoring such images by leveraging deep learning techniques. Specifically, to accurately identify scratches in images, we train a classification network on a large dataset of paired old and repaired photos. Additionally, we employ a Fourier convolution-based neural network to repair the damaged areas of the images. Our results show that our approach outperforms existing methods in terms of both objective metrics and visual quality. We believe that our work has the potential to preserve valuable memories and historical artifacts for future generations.
Images, being significant carriers of memories and information, are valued by people. To restore images, it is necessary to perform noise reduction processing to eliminate noise generated by camera equipment and other factors. Traditional denoising technology such as wavelet transform is used to help engineer restore a image. And in recent years, the introduction of convolutional neural networks has accelerated the progress of noise reduction research. Many classic models have been developed by researchers using U-shaped networks and other techniques. Researchers often use multiscale approaches to obtain multiple feature maps and enhance their network with these features. Our work enhanced denoising network by introducing large convolutions, small convolutions, and Fast Fourier convolutions to capture feature information at different scales. Additionally, we used an SE block to introduce attention mechanisms into the network. As evidenced by experimental results, our network achieved outstanding performance.
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