Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …

Within the domain of image processing, a wide array of methodologies is dedicated to tasks
including denoising, enhancement, segmentation, feature extraction, and classification …

During the past decade, deep learning is one of the essential breakthroughs made in
artificial intelligence. In particular, it has achieved great success in image processing …

Due to the wavelength-dependent light absorption and scattering, the raw underwater
images are usually inevitably degraded. Underwater image enhancement (UIE) is of great …

Most recently, deep learning-based visual detection has attracted rapidly increasing
attention paid to marine organisms, thereby expecting to significantly benefit ocean ecology …

Owing to the flexible architectures of deep convolutional neural networks (CNNs) are
successfully used for image denoising. However, they suffer from the following drawbacks:(i) …

Blind and universal image denoising consists of using a unique model that denoises images
with any level of noise. It is especially practical as noise levels do not need to be known …

In recent years, deep learning techniques are employed in the mammography processing
field to reduce radiologists' costs. Existing breast mass classification systems are …

Significant advances in video compression systems have been made in the past several
decades to satisfy the near-exponential growth of Internet-scale video traffic. From the …