With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

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 …

Synthesizing high-fidelity complex images from text is challenging. Based on large
pretraining, the autoregressive and diffusion models can synthesize photo-realistic images …

In recent advancements in high-fidelity image generation Denoising Diffusion Probabilistic
Models (DDPMs) have emerged as a key player. However their application at high …

Deep convolutional neural networks can effectively improve the performance of single-
image super-resolution reconstruction. Deep networks tend to achieve better performance …

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …