MicroDeblur: Image Motion Deblurring on Microcontroller-based Vision Systems

Abstract

This paper introduces MicroDeblur, an on-device image motion deblur solution for resource-constrained microcontroller-based vision systems. Although motion blurs caused by the movement or shake of the device (camera) are pervasive in embedded, IoT, and mobile devices, it has been considered a hard nut to crack for many microcontrollers with extremely-limited resources (e.g., hundreds of KB of RAM). To tackle this problem, we combine the DNN (deep neural network) motion deblur method with the classical motion deblur approach and take the best of both worlds, i.e., 1) powerful pattern recognition ability of DNNs and 2) simplicity and stability of matrix-based classical algorithms. To deblur an image, MicroDeblur takes three steps: 1) blur kernel estimation, 2) blur image transformation, and 3) iterative clear image restoration. We propose 1) depth-independent convolution that efficiently estimates the blur kernel (pattern) and 2) Toeplitz-based motion blur modeling that enhances the time and space complexity of the deblurring process by and , respectively, compared to the existing methods. To the best of our knowledge, MicroDeblur is the first self-sufficient blind deconvolution solution for a stand-alone microcontroller that does not rely on extra hardware or external systems. We implement MicroDeblur on an ARM Cortex-M4F, achieving a competitive quality of deblurred images using 187x and 429x smaller memory and energy, respectively, compared to high-end GPU-based solutions.