Blur image classification is essential for computer vision applications, including image quality assessment, surveillance and medical imaging systems. This study proposes a method to classify different types of blur: sharp, Gaussian blur, motion blur, and defocus blur, using the DenseNet121 architecture.

This paper is structured as follows. The following section presents related work. Section 3 summarizes the characteristics of the two datasets utilized in the model and the system’s overall architecture for image-based disease diagnosis. Section 4 provides our experimental results that compare the performance metrics with other studies.