Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain
Takanori Fujisawa, Masaaki Ikehara · 2016
Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified highpass filter (HPF) for feature extraction to estimate a blur kernel. This problem can consider the kernel estimation in the region where salient edges are not present and improve the quality of the estimated kernel. Our approach also accelerates the deconvolution process by utilizing a conjugate gradient method in a frequency domain. This process eliminates costly convolution operations from the iterative updating and reduces the calculation time. Evaluation for 20 test images shows our framework not only performs faster than conventional frameworks but also improves the quality of recovered images.