Glaucoma is one of the leading causes of blindness worldwide, where early and precise diagnosis is critical for effective treatment. We explore the potential of deep learning to generate a Visual Field Grayscale Map (GM) directly from a Conventional Fundus Image (CFI) at the same time point, enabling an intuitive representation of visual field defects for the diagnosis of glaucoma. The Humphrey Visual Field (HVF) test is a standard method for assessing visual field defects, but its dependency on active participation and prolonged testing time limits its reliability, particularly in elderly patients. To address these challenges, we propose CFI2GM-GAN, a novel deep learning framework that directly generates GM from CFI, offering a faster and cost-effective alternative while complementing the HVF test. Our method incorporates dataset partitioning based on defect severity to mitigate class imbalance, attention-guided fusion techniques for improved prediction reliability, and a refined loss function customized to fundus image characteristics.

Generative adversarial network (GAN)-based methods recover perceptually pleasant details in super resolution (SR), but they pertain to structural distortions. Recent study alleviates such structural distortions by attaching a gradient branch to the generator. However, this method compromises the perceptual details. In this paper, we propose a sparse gradient-guided attention generative adversarial network (SGAGAN), which incorporates a modified residual-in-residual sparse block (MRRSB) in the gradient branch and gradient-guided self-attention (GSA) to suppress structural distortions. Compared to the most frequently used block in GAN-based SR methods, i.e., residual-in-residual dense block (RRDB), MRRSB reduces computational cost and avoids gradient redundancy. In addition, GSA emphasizes the highly correlated features in the generator by guiding sparse gradient. It captures the semantic information by connecting the global interdependencies of the sparse gradient features in the gradient branch and the features in the SR branch. Experimental results show that SGAGAN relieves the structural distortions and generates more realistic images compared to state-of-the-art SR methods. Qualitative and quantitative evaluations in the ablation study show that combining GSA and MRRSB together has a better perceptual quality than combining self-attention alone.