Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Wuhan, China.
Hainan University, School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hai, China.
J Biomed Opt. 2022 May;27(5). doi: 10.1117/1.JBO.27.5.056503.
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.
共聚焦内镜图像经常会出现失真，导致图像质量下降和信息丢失，增加了诊断的难度，甚至导致误诊。在诊断前，评估图像质量并过滤诊断价值较低的图像非常重要。
我们提出了一种基于韦伯定律和局部描述符的共聚焦内镜图像无参考图像质量评估 (IQA) 方法。该方法通过捕捉图像的感知结构，可以检测图像降级的严重程度。
我们创建了一个包含 642 张共聚焦内镜图像的新数据集，以验证所提出方法的性能。然后，我们进行了广泛的实验，比较了所提出方法与其他最先进的 IQA 方法的准确性和速度。
实验结果表明，所提出的方法在 SROCC 方面达到了 0.85，优于其他 IQA 方法。
鉴于其在主观质量评估方面具有较高的一致性，该方法可以在实际应用中筛选高质量的图像，有助于诊断。
