5 accepted papers
As super-resolution (SR) techniques advance, we observe a growing distrust of evaluation metrics in recent SR research. An inconsistency often emerges between certain evaluation criteria and human perceptual preference. Although current SR research employs varying metrics to evaluate SR performance,
Single image super-resolution (SISR), especially in the real world, usually builds a large amount of LR-HR image pairs to learn representations that contain rich textural and structural information. However, relying on massive data for model training not only reduces training efficiency, but also ca…
Deep learning (DL) based image super-resolution (SR) tech-niques have been well investigated for recent years. However, studies dedicated to SR image quality assessment (SR-IQA) have not been fully developed, which is even more difficult if pristine high-resolution (HR) images are lacking as a refer…
In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean result from the captured images heavily depends on the ability of restoration methods and the quality…
Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fai…