On a 640-KB-SRAM STM32H563ZI MCU, an INT8-quantized super-resolution network simultaneously reconstructs high-resolution depth and intensity from a VL53L9CX SPAD sensor
This work presents a two-branch super-resolution deep-learning framework that simultaneously upscales low-resolution depth and intensity images from a consumer-grade VL53L9CX SPAD time-of-flight sensor, using a two-stage PixelShuffle reconstruction head and simplified sensor firmware to control activation memory, then exports the model to ONNX, quantizes it to INT8, generates C code, and deploys it on a bare-metal STM32H563ZI MCU with only 640 KB SRAM and 2 MB Flash for fully on-device super-resolution inference.