Image Quality
Image quality assessment (IQA) focuses on objectively measuring and improving the visual fidelity of images, crucial for various applications from medical imaging to autonomous driving. Current research emphasizes developing robust no-reference IQA methods, often employing deep learning architectures like transformers and convolutional neural networks, and exploring the use of generative AI models for image enhancement and compression. These advancements are significant because they enable automated quality control, improved diagnostic accuracy in healthcare, and more efficient data management across numerous fields.
Papers
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment
Zitong Xu, Huiyu Duan, Guangji Ma, Liu Yang, Jiarui Wang, Qingbo Wu, Xiongkuo Min, Guangtao Zhai, Patrick Le Callet
Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion
Dong Zhang, Kwang-Ting Cheng
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Zilin Wang, Shengqi Chen, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Guohua Wu, Yuan Tang, Jiayun Chen
A CT Image Denoising Method Based on Projection Domain Feature
Mengyu Sun, Dimeng Xia, Shusen Zhao, Weibin Zhang, Yaobin He
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
Siyi Pan, Baoliang Chen, Danni Huang, Hanwei Zhu, Lingyu Zhu, Xiangjie Sui, Shiqi Wang
Acquire Precise and Comparable Fundus Image Quality Score: FTHNet and FQS Dataset
Zheng Gong, Zhuo Deng, Run Gan, Zhiyuan Niu, Lu Chen, Canfeng Huang, Jia Liang, Weihao Gao, Fang Li, Shaochong Zhang, Lan Ma