Convolutions are the fundamental building blocks of CNNs. The fact that their weights are
spatially shared is one of the main reasons for their widespread use, but it is also a major …

High-resolution digital images are usually downscaled to fit various display screens or save
the cost of storage and bandwidth, meanwhile the post-upscaling is adopted to recover the …

Deep convolutional neural network based image super-resolution (SR) models have shown
superior performance in recovering the underlying high resolution (HR) images from low …

Labeling data instances is an important task in machine learning and visual analytics. Both
fields provide a broad set of labeling strategies, whereby machine learning (and in particular …

Most convolutional neural networks use some method for gradually downscaling the size of
the hidden layers. This is commonly referred to as pooling, and is applied to reduce the …

Image downscaling is one of the most classical problems in computer vision that aims to
preserve the visual appearance of the original image when it is resized to a smaller scale …

The digital age has brought a significant increase in video traffic. This traffic growth, driven
by rapid internet advancements and a surge in multimedia applications, presents both …

Image rescaling is a commonly used bidirectional operation, which first downscales high-
resolution images to fit various display screens or to be storage-and bandwidth-friendly, and …

In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the
analysis of acoustic data to detect and understand previously unknown errors in the …

Retinal related diseases are the leading cause of vision loss, and severe retinal lesion
causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases …