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 …

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 …

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 …

Due to the generally low spatial resolution of hyperspectral images (HSIs), early
multispectral images lacked corresponding panchromatic bands, and as a result, fusion …

The use of deep convolutional neural networks has significantly improved the performance
of super-resolution. Employing deeper networks to enhance the non-linear map** …

Deep learning has excelled in single-image super-resolution (SISR) applications, yet the
lack of interpretability in most deep learning-based SR networks hinders their applicability …

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 …