Convolutional neural networksHigh-temperature measurementDigital image correlationNon-contact measurement method at elevated temperatures has been widely studied, which provides an efficient means for evaluating properties of high-temperature materials. However, such high temperature environment induced challenges ...
DEEP CONVOLUTIONAL DENOISING 本部分讨论以CNN作为Eq.3中的g函数 每一层g函数都运用了多个有可学习参数的空间核(spatial kernel,chatgpt解释这就是filter的意思)函数 论文给出了完整的神经网络结构: architecture 总体分成了三个部分: 预处理(preprocessing) 过滤(filtering) 后处理(postprocessing) Network architecture...
Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), ...
In this paper, we proposed a new CNN model, namely FFDNet, for fast, effective and flexible discriminative denoising. To achieve this goal, several techniques were utilized in network design and training, such as the use of noise level map as input and denoising in downsampled sub-images spac...
When the noise level\sigmais unknown, the denoising method should enable the user to adaptively make a trade-off between noise suppression and texture protection. The fast and flexible denoising convolutional neural network (FFDNet) [107] was introduced to satisfy these desirable characteristics. In ...
A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a sign... Q An,W Chen,W Shao - Diagnostics (...
This example shows how to generate plain CUDA® MEX from MATLAB® code and denoise grayscale images by using the denoising convolutional neural network (DnCNN [1]). The pretrained denoising network estimates the noise in a noisy image and then removes it, resulting in a clearer, denoised ...
neural networks have catalyzed substantial advancements in learning-based denoising methods8,9,10,11, marking a significant evolution in this field. For instance, the Denoising Convolutional Neural Network (DnCNN)12incorporates residual learning (RL) and batch normalization, enabling faster convergence and...
Convolutional Neural Network has achieved great success in image denoising. The conventional methods usually sense those beyond scope contextual info at the expense of the receptive filed shrinking, which easily lead to multiple limitations. In this paper, we have proposed a concise and efficient conv...
In this paper, a deep convolutional neural network was proposed for image denoising, where residual learning is adopted to separating noise from noisy observation. The batch normalization and residual learning are integrated to speed up the training process as well as boost the denoising performance....