No-reference image quality assessment for confocal endoscopy images with perceptual local descriptor

Confocal endoscopy images often suffer distortions, resulting in image quality degradation and information loss, increasing the difficulty of diagnosis and even leading to misdiagnosis. It is important to assess image quality and filter images with low diagnostic value before diagnosis.

We propose a no-reference image quality assessment (IQA) method for confocal endoscopy images based on Weber's law and local descriptors. The proposed method can detect the severity of image degradation by capturing the perceptual structure of an image.

We created a new dataset of 642 confocal endoscopy images to validate the performance of the proposed method. We then conducted extensive experiments to compare the accuracy and speed of the proposed method with other state-of-the-art IQA methods.

Experimental results demonstrate that the proposed method achieved an SROCC of 0.85 and outperformed other IQA methods.

Given its high consistency in subjective quality assessment, the proposed method can screen high-quality images in practical applications and contribute to diagnosis.