diffuse 网络的损失是有reconstructed irradianced(在乘上albedo之前)和albedo-factorized reference image之间的l_1指标进行计算的 specular的loss就在对数域中进行计算 network使用tensorflow的ADAM优化器进行优化 learning rate 1e-5 mini-batch size 5 预先在750K轮次的训练(大概1.5天) M6000GPU上进行训练 最后进行大...
Image denoisingConvolutional 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 ...
Recently, convolutional neural network (CNN)-based methods have achieved impressive performance on image denoising. Notably, CNN with deeper and thinner structures is more flexible to extract the image details. However, direct stacking some existing networks is difficult to achieve satisfactory denoising ...
In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered ...
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...
An application of deep dual convolutional neural network for enhanced medical image denoising This work investigates the medical image denoising (MID) application of the dual denoising network (DudeNet) model for chest X-ray (CXR). The DudeNet model... A Sahu,KPS Rana,V Kumar - 《Medical &...
Presently, a lot of research is going on in the area of image denoising and found to be an emerging one. In this paper, a pretrained feed-forward denoising using convolutional neural networks (DnCNNs) is considered and found to be better than conventional filters used. The results are ...
A Convolutional Neural Network (CNN) is a multilayer network structure that includes single-layer convolutional neural networks. It utilizes operations such as convolution, nonlinear transformation, and downsampling to process input data, particularly successful in image feature representation and classificatio...
To mitigate theses drawbacks in SDC, we therefore, propose a hierarchical dense dilated deep pyramid feature extraction through convolution neural network (CNN) for single image crowd counting (HDPF). It comprises of three modules: general feature extraction module (GFEM), deep pyramid feature ...
Compared with the traditional image denoising method, although the convolutional neural network (CNN) has better denoising performance, there is an important issue that has not been well resolved: the residual image obtained by learning the difference between noisy image and clean image pairs contains...