Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment.
In this paper, we propose an FR-IQA model with the quadratic sum of the GM and the LOG signals, which obtains good performance in image quality estimation considering shift-insensitive property for not well-registered reference and distortion image pairs.
Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features.
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is particularly challenging due to the absence of knowledge about the reference image and distortion type.
We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD).