High-resolution image transmission is required in safety helmet detection problems in the
construction industry, which makes it difficult for existing image detection methods to achieve …

In recent years, constructing various deep convolutional neural networks (CNNs) for single-
image super-resolution (SISR) tasks has made significant progress. Despite their high …

Transcranial focused ultrasound (tFUS) has emerged as a new mode of non-invasive brain
stimulation (NIBS), with its exquisite spatial precision and capacity to reach the deep regions …

Abstract Background and objective Magnetic Resonance Image (MRI) analysis can provide
anatomical examination of internal organs, which is helpful for diagnosis of the disease …

Existing crack recognition methods based on deep learning often face difficulties when
detecting cracks in complex scenes such as brake marks, water marks, and shadows. The …

U-Net series models have achieved considerable success in various fields such as image
segmentation and image classification. However, the decoders in these models often use …

Deep neural networks can significantly improve the quality of super-resolution. However,
previous work has made insufficient use of low-resolution scale features and channel-wise …

Traditional Chinese landscape painting is prone to low-resolution image issues during the
digital protection process. To reconstruct high-quality images from low-resolution landscape …

Single image super-resolution (SISR) refers to the task restoring a high-resolution (HR)
image from its low-resolution (LR) counterpart. Deep convolutional neural networks (CNNs) …

In recent years, convolutional neural networks have been successfully applied to single
image super-resolution (SISR) tasks, making breakthrough progress both in accuracy and …