Improving acne severity detection: a GAN framework with contour accentuation for image deblurring - Mascarenhas, Philomina Princiya; Sannidhan, M. S.; Pinto, Ancilla J.; Camero, Dabis; Martis, Jason Elroy (Frontiers in Bioinformatics, 2025)
Teledermatology, a growing field of telemedicine, is widely used to diagnose skin conditions like acne, especially in young adults. Accurate diagnosis depends on clear images, but blurring is a common issue in most images. In particular, for acne images, it obscures acne spots and facial contours, leading to inaccurate diagnosis. Traditional methods to address blurring fail to recover fine details, making them unsuitable for teledermatology. To resolve this issue, the study proposes a framework based on generative networks. It comprises three main steps: the Contour Accentuation Technique, which outlines facial features to create a blurred sketch; a deblurring module, which enhances the sketch’s clarity; and an image translator, which converts the refined sketch into a color photo that effectively highlights acne spots. Tested on Acne Recognition Dataset, the framework achieved an SSIM of 0.83, a PSNR of 22.35 dB, and an FID score of 10.77, demonstrating its ability to produce clear images for accurate acne diagnosis. Further, the details of research can be found on the project homepage at: https://github.com/Princiya1990/CATDeblurring.