Image classification has been a popular task due to its feasibility in
real-world applications. Training neural networks by feeding them RGB images
has demonstrated success over it. Nevertheless, improving the classification
accuracy and computational efficiency of this process continues to present
challenges that researchers are actively addressing. A widely popular embraced
method to improve the classification performance of neural networks is to
incorporate data augmentations during the training process. Data augmentations
are simple transformations that create slightly modified versions of the
training data and can be very effective in training neural networks to mitigate
overfitting and improve their accuracy performance. In this study, we draw
inspiration from high-boost image filtering and propose an edge
enhancement-based method as means to enhance both accuracy and training speed
of neural networks. Specifically, our approach involves extracting high
frequency features, such as edges, from images within the available dataset and
fusing them with the original images, to generate new, enriched images. Our
comprehensive experiments, conducted on two distinct datasets CIFAR10 and
CALTECH101, and three different network architectures ResNet-18, LeNet-5 and
CNN-9 demonstrates the effectiveness of our proposed method.

通过在训练过程中引入高频特征提取和边缘增强的方法，我们提出了一种增强神经网络的准确性和训练速度的方法。实验证明了我们的方法在两个不同的数据集 CIFAR10 和 CALTECH101 以及三种不同的网络架构 ResNet-18，LeNet-5 和 CNN-9 上的有效性。

有效图像分类的图像边缘增强

Image edge enhancement for effective image classification

Infrared and visible image fusion task aims to generate a fused image which
contains salient features and rich texture details from multi-source images.
However, under complex illumination conditions, few algorithms pay attention to
the edge information of local regions which is crucial for downstream tasks. To
this end, we propose a fusion network based on the local edge enhancement,
named LE2Fusion. Specifically, a local edge enhancement (LE2) module is
proposed to improve the edge information under complex illumination conditions
and preserve the essential features of image. For feature extraction, a
multi-scale residual attention (MRA) module is applied to extract rich
features. Then, with LE2, a set of enhancement weights are generated which are
utilized in feature fusion strategy and used to guide the image reconstruction.
To better preserve the local detail information and structure information, the
pixel intensity loss function based on the local region is also presented. The
experiments demonstrate that the proposed method exhibits better fusion
performance than the state-of-the-art fusion methods on public datasets.

本文提出了一种基于本地边缘增强的融合网络，该网络可有效增强图像的边缘信息，并具有较好的图像融合性能。

LE2Fusion：一种用于红外和可见光图像融合的新型局部边缘增强模块

LE2Fusion: A novel local edge enhancement module for infrared and  visible image fusion

In this work, we present Eformer - Edge enhancement based transformer, a
novel architecture that builds an encoder-decoder network using transformer
blocks for medical image denoising. Non-overlapping window-based self-attention
is used in the transformer block that reduces computational requirements. This
work further incorporates learnable Sobel-Feldman operators to enhance edges in
the image and propose an effective way to concatenate them in the intermediate
layers of our architecture. The experimental analysis is conducted by comparing
deterministic learning and residual learning for the task of medical image
denoising. To defend the effectiveness of our approach, our model is evaluated
on the AAPM-Mayo Clinic Low-Dose CT Grand Challenge Dataset and achieves
state-of-the-art performance, $i.e.$, 43.487 PSNR, 0.0067 RMSE, and 0.9861
SSIM. We believe that our work will encourage more research in
transformer-based architectures for medical image denoising using residual
learning.

本文提出了 Eformer - Edge enhancement based transformer，这是一种新型的结构，利用变压器块构建编码器 - 解码器网络进行医学图像去噪。变压器块中使用非重叠窗口自注意力，以减少计算要求。该文进一步将可学习的 Sobel-Feldman 运算符并入图像中以增强边缘，并提出了将它们在我们的结构的中间层中连接的有效方法。实验分析比较了确定性学习和残差学习用于医学图像去噪任务。为了证明我们方法的有效性，我们的模型在 AAPM-Mayo Clinic 低剂量 CT 大挑战数据集上进行评估，并达到最新的性能，即 43.487 PSNR，0.0067 RMSE 和 0.9861 SSIM。我们相信我们的工作将鼓励更多的研究者使用残差学习开发基于变压器结构的医学图像去噪方法。

Eformer：基于边缘增强的医学图像去噪 Transformer

Eformer: Edge Enhancement based Transformer for Medical Image Denoising

In the past few decades, to reduce the risk of X-ray in computed tomography
(CT), low-dose CT image denoising has attracted extensive attention from
researchers, which has become an important research issue in the field of
medical images. In recent years, with the rapid development of deep learning
technology, many algorithms have emerged to apply convolutional neural networks
to this task, achieving promising results. However, there are still some
problems such as low denoising efficiency, over-smoothed result, etc. In this
paper, we propose the Edge enhancement based Densely connected Convolutional
Neural Network (EDCNN). In our network, we design an edge enhancement module
using the proposed novel trainable Sobel convolution. Based on this module, we
construct a model with dense connections to fuse the extracted edge information
and realize end-to-end image denoising. Besides, when training the model, we
introduce a compound loss that combines MSE loss and multi-scales perceptual
loss to solve the over-smoothed problem and attain a marked improvement in
image quality after denoising. Compared with the existing low-dose CT image
denoising algorithms, our proposed model has a better performance in preserving
details and suppressing noise.

该研究提出基于边缘增强的密集连接卷积神经网络（EDCNN）用于低剂量 CT 图像去噪，并引入混合损失函数以增强模型去噪效果，取得了较好的效果。