Blind motion deconvolution for binary images

Binary images are prevalent in digital systems and have a wide range of applications including texts, fingerprint recognition, handwritten signatures, stellar astronomy, barcodes, and vehicle license plates. The recorded binary images are often degraded by blur and additive noise due to environmental effects and imperfections in the imaging system. In this paper, we study the problem of recovering the sharp binary image and the blur kernel from the motion degraded observation. We propose a new minimization model by using the binary prior of image pixel and the l₀ norm of image gradient to enforce the estimated image to be binary and the image gradient to be sparse respectively. An effective numerical optimization algorithm is applied for solving the proposed model. Extensive experiments for blind binary image deconvolution demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).