By benefiting from perceptual losses, recent studies have improved significantly the
performance of the super-resolution task, where a high-resolution image is resolved from its …

Diabetic retinopathy (DR) is a leading cause of permanent blindness among the working-
age people. Automatic DR grading can help ophthalmologists make timely treatment for …

Road extraction from optical remote sensing images has many important application
scenarios, such as navigation, automatic driving and road network planning, etc. Current …

Real-world single image Super-Resolution (SR) aims to enhance the resolution and
reconstruct High-Resolution (HR) details of real Low-Resolution (LR) images. This is …

Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-
scale screening in the radiology workflow. Supervised deep networks take for granted a …

Multi-task pixel perception is one of the most important topics in the field of machine
intelligence. Inspired by the observation of cross-task interdependencies of visual patterns …

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR)
and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling …

Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE
segmentation is vital for disease diagnosis and treatment, but automatic segmentation is …

Text image super-resolution is a pre-processing of scene text recognition, which aims to
improve the visual quality of text from low-resolution images. However, existing super …

Radiation dose reduction of computed tomography (CT) is an important research topic due
to the potential risk of X-rays. However, low-dose CT (LDCT) images inevitably have a noise …