CFRNet: Cycle-Consistent Fixed-Point Training for Real-Time Blind Face Restoration on Consumer Embedded NPUs
Researchers have developed CFRNet, a new model for real-time blind face restoration on consumer embedded NPUs. This model utilizes a novel Cycle-Consistent Fixed-Point Training (CCFP) method, which trains the network to act as a fixed-point operator, improving image quality without adding inference cost. CFRNet achieves superior perceptual scores and PSNR/SSIM metrics compared to baselines retrained under similar deployment constraints. The model demonstrates efficient performance, running in approximately 23ms per cycle on a HiSilicon Hi3402 NPU and is capable of real-time operation on in-car driver-monitoring systems. AI
IMPACT Enables high-quality face restoration on low-power, embedded devices, potentially improving real-time applications like driver monitoring.