1 citations · 2 across the 4 of their papers we have counts for
4 papers
Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment
Yixiao Li, Xiaoyuan Yang, Guanghui Yue +6
Many super-resolution (SR) algorithms have been proposed to increase image resolution. However, full-reference (FR) image quality assessment (IQA) metrics for comparing and evaluat…
CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP
Yirui Zeng, Jun Fu, Hadi Amirpour +5
Blind dehazed image quality assessment (BDQA), which aims to accurately predict the visual quality of dehazed images without any reference information, is essential for the evaluat…
Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment
Jun Fu, Wei Zhou, Qiuping Jiang +2
Recently, textual prompt tuning has shown inspirational performance in adapting Contrastive Language-Image Pre-training (CLIP) models to natural image quality assessment. However,…
Deep Bi-directional Attention Network for Image Super-Resolution Quality Assessment
Yixiao Li, Xiaoyuan Yang, Jun Fu +2
There has emerged a growing interest in exploring efficient quality assessment algorithms for image super-resolution (SR). However, employing deep learning techniques, especially d…