An Energy-Scalable Accelerator for Blind Image Deblurring

作者：Raina Priyanka^*; Tikekar Mehul; Chandrakasan Anantha P

来源：IEEE Journal of Solid-State Circuits, 2017, 52(7): 1849-1862.

DOI：10.1109/JSSC.2017.2682842

• Deconvolution
• energy scalability
• gradient projection solver
• hardware accelerator
• image deblurring

Camera shake is a common 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 paper presents the first hardware accelerator for kernel estimation for image deblurring applications. Our approach, using a multi-resolution iteratively reweighted least squares deconvolution engine with DFT-based matrix multiplication, a high-throughput image correlator, and a high-speed selective update-based gradient projection solver, achieves a 78x reduction in kernel estimation runtime, and a 56x reduction in total deblurring time for a 1920x1080 image, enabling quick feedback to the user. Configurability in kernel size and number of iterations gives up to ten times energy scalability, allowing the system to trade off runtime with image quality. The test chip, fabricated in TSMC 40-nm CMOS technology, consumes 105 mJ for kernel estimation running at 83 MHz and 0.9 V, making it suitable for integration into mobile devices.

• 出版日期2017-7