An energy-scalable accelerator for blind image deblurring

Abstract

Camera shake is the leading cause of blur in cell-phone camera images. Removing blur requires deconvolving the blurred image with a kernel which is typically unknown and needs to be estimated from the blurred image. This kernel estimation is computationally intensive and takes several minutes on a CPU which makes it unsuitable for mobile devices. This work presents the first hardware accelerator for kernel estimation for image deblurring applications. Our approach, using a multi-resolution IRLS deconvolution engine with DFT based matrix multiplication, a high-throughput image correlator and a high-speed selective update based gradient projection solver, achieves a 78× reduction in kernel estimation runtime, and a 56× reduction in total deblurring time for a 1920×1080 image enabling quick feedback to the user. Configurability in kernel size and number of iterations gives up to 10× energy scalability, allowing the system to trade-off runtime with image quality. The test chip, fabricated in 40 nm CMOS, consumes 105 mJ for kernel estimation running at 83 MHz and 0.9 V, making it suitable for integration into mobile devices.