Real-world image denoising is a practical image restoration problem that aims to obtain
clean images from in-the-wild noisy inputs. Recently, the Vision Transformer (ViT) has …

For computer vision and image content understanding, the low-light image becomes an
obvious challenge as it suffers from poor contrast and illumination. Hence, low-light image …

Lightweight object detection can promote intelligent robotics to recognize surroundings
objects with limited computational resources, and thus receives increasing attention in …

Super-Resolution (SR) algorithms aim to enhance the resolutions of images. Massive deep-
learning-based SR techniques have emerged in recent years. In such case, a visually …

Actual image super-resolution is an extremely challenging task due to complex
degradations existing in the image. To solve this problem, two dominant methodologies …

Existing facial expression recognition (FER) methods are mainly devoted to learning
discriminative features from high-resolution images. However, when applied to low …

An increasing number of image perception models are being utilized in the field of
autonomous driving. During nighttime driving, the visual perception capabilities of a single …

Despite remarkable progress in single-image super-resolution based on neural networks,
the results are not ideal when applied to real-world images, because the real-world …

Compressed video super-resolution (VSR) is employed to generate high-resolution (HR)
videos from low-resolution (LR) compressed videos. Recently, some compressed VSR …

Deep learning technologies have demonstrated their effectiveness in removing cloud cover
from optical remote-sensing images. Convolutional neural networks (CNNs) exert …