- Color restoration of mural images based on a reversible neural ...🔍
- NSF Award Search🔍
- The Level Weighted Structural Similarity Loss🔍
- A unified loss formula to train neural networks to classify images and ...🔍
- Non|Local Recurrent Network for Image Restoration🔍
- Noise2Noise🔍
- SAR Image Despeckling Using a Convolutional Neural Network🔍
- Improving Loss Function for a Deep Neural Network for Lesion ...🔍
Loss Functions for Image Restoration With Neural Networks
Color restoration of mural images based on a reversible neural ...
To address this issue, Gomez et al. [18] first proposed the reversible residual network to mitigate memory consumption in deep neural network ...
NSF Award Search: Award # 2045489 - CAREER: Seeing Through ...
... image restoration techniques by developing novel end-to-end trainable deep convolutional neural networks and corresponding loss functions.
The Level Weighted Structural Similarity Loss: A Step Away from MSE
SSIM as a loss function to guide the image reconstruction. Models with ... Loss functions for image restoration with neural networks. IEEE. Transactions ...
A unified loss formula to train neural networks to classify images and ...
In the event that both images are from similar class, if the Euclidean distance is within the Similarity Margin, the model will not incur any ...
Non-Local Recurrent Network for Image Restoration - NIPS papers
Deep neural networks have been prevalent for image restoration. The ... We use Adam optimizer to minimize the loss function. We set the initial ...
Noise2Noise: Learning Image Restoration without Clean Data
Similar observations can be made about other loss functions. For instance, the L1 loss recovers the median of the targets, meaning that neural networks can be ...
Index Terms—Synthetic aperture radar, despecking, denoising, image restoration. ... Loss functions form an important and integral part of learn- ing process ...
Improving Loss Function for a Deep Neural Network for Lesion ...
We propose to improve a state-of-the-art encoder-decoder based model for image segmentation, named as Focal-Binary Cross Entropy (BCE)- ...
Focal Network for Image Restoration - mediaTUM
the same resolution, loss functions are given by: Ls = 1. P. ∥Î− G∥1. (6). Lf ... volutional neural networks with octave convolution. In Pro- ceedings ...
Literature Review on Image Restoration - IOPscience
1)In the algorithm of image restoration based on deep learning, the selection of loss function plays a key role in the process of network building and training, ...
A novel perceptual loss function for single image super-resolution
The commonly used per-pixel MSE loss function captures less perceptual difference and tends to make the super-resolved images overly smooth, while the ...
Can We Integrate Spatial Verification Methods into Neural Network ...
... NN loss functions, and we test our novel spatially enhanced loss functions ... Kautz, 2017: Loss functions for image restoration with neural networks. IEEE ...
Perceptual Loss Function | Saturn Cloud
... image generation tasks. It leverages the power of pre-trained convolutional neural networks (CNNs) to compare high-level features between the target and the ...
Image restoration for synthetic aperture systems with a non-blind ...
A synthetic aperture convolutional neural network (CNN) is trained as a denoiser prior to restoring the image. By improving the half-quadratic splitting ...
Prior-Guided Deep Neural Networks for Image Restoration Tasks
The resulting textures can be used in the loss function of the neural network during training, which leads to better estimation of the high- ...
Function f parameters : {Wk , bk }k . 3 / 26. Page 6. Convolutional neural network. • Replace the ...
What are the most effective loss functions for neural networks?
Reconstruction Loss: BCE: Measures binary pixel-wise differences. SSIM: Measures perceptual difference in images. Embedding Loss: Contrastive ...
What are some common loss functions used in training computer ...
Application: Cross-Entropy Loss is extensively used in tasks like image classification and object detection, where the model needs to ...
Training a Task-Specific Image Reconstruction Loss - Academia.edu
In recent years, convolutional neural network (CNN) based models have achieved great performance on SISR task. Despite the breakthroughs achieved by using CNN ...
Motion Blur Image Restoration by Multi-Scale Residual Neural ...
The principle is not to rely on fuzzy kernel estimation, and to adjust the weight parameters and loss function by constructing a neural network to achieve the ...