Description
SRGAN Residual Block is a residual block used in the SRGAN generator for image super-resolution. It is similar to standard residual blocks, although it uses a PReLU activation function to help training (preventing sparse gradients during GAN training).
Papers Using This Method
Super-Resolution Generative Adversarial Networks based Video Enhancement2025-05-14Uncertainty Estimation for Super-Resolution using ESRGAN2024-12-19Deep Learning-Based CKM Construction with Image Super-Resolution2024-10-28Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint2024-04-06Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks2024-01-31Texture and Noise Dual Adaptation for Infrared Image Super-Resolution2023-11-15Guided Frequency Loss for Image Restoration2023-09-27A comparative analysis of SRGAN models2023-07-18A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models2022-10-12Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation2022-09-07Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images2022-04-06High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss2021-07-21Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks2021-05-17More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination2021-01-07Super-resolution Guided Pore Detection for Fingerprint Recognition2020-12-10Fully Quantized Image Super-Resolution Networks2020-11-29Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset2020-10-20Attaining Real-Time Super-Resolution for Microscopic Images Using GAN2020-10-09Journey Towards Tiny Perceptual Super-Resolution2020-07-08Perceptual Extreme Super Resolution Network with Receptive Field Block2020-05-26