Dual-channel lightweight GAN for enhancing color retinal images with noise suppression and structural protection.
Journal:
Journal of the Optical Society of America. A, Optics, image science, and vision
PMID:
39889019
Abstract
As we all know, suppressing noise while maintaining detailed structure has been a challenging problem in the field of image enhancement, especially for color retinal images. In this paper, a dual-channel lightweight GAN named dilated shuffle generative adversarial network (DS-GAN) is proposed to solve the above problems. The lightweight generator consists of the RB branch used in the red-blue channels and the GN branch used in the green channel. The branches are then integrated with a cat function to generate enhanced images. The RB branch cascades six identical RB-enhanced modules and adds skip connections. The structure of the GN branch is similar to that of the RB branch. The generator simultaneously leverages the local context extraction capability of the normal convolution and the global information extraction capability of the dilated convolution. In addition, it facilitates the fusion and communication of feature information between channels through channel shuffle. Additionally, we utilize the lightweight image classification model ShuffleNetV2 as a discriminator to distinguish between enhanced images and corresponding labels. We also constructed a dataset for color retinal image enhancement by using traditional methods and a hybrid loss function by combining the MS-SSIM and perceptual loss for training the generator. With the proposed dataset and loss function, we train the DS-GAN successfully. We test our method on four publicly available datasets (Messidor, DIARETDB0, DRIVE, and FIRE) and a clinic dataset from the Tianjin Eye Hospital (China), and compare it with six existing image enhancement methods. The results show that the proposed method can simultaneously suppress noise, preserve structure, and enhance contrast in color retinal image enhancement. It gets better results than the compared methods in all cases. Furthermore, the model has fewer parameters, which provides the possibility of real-time image enhancement for portable devices.