Blind Image Deblurring Using Gradient Prior and Sparse Prior

To precisely estimate blur kernels (BKs) and obtain high-quality latent images (LIs), we present an efficient blind image deblurring (BID) algorithm via a gradient prior and an enhanced sparse prior. The proposed algorithm decomposes BID into two phases: multiscale blur estimation (MSBE) and image deconvolution (ID). The former focuses on precise BKs, whereas the latter focuses on high-quality LIs. In the MSBE phase, significant image gradients and an enhanced sparse prior are used to estimate BKs. To this end, we build an MSBE model using a hyper-Laplacian gradient (HLG) prior and sparsity-inducing l2,1-regularization. l2,1-regularization imposes enhanced sparsity on the BKs to be estimated, and the HLG prior characterizes the true distribution of image gradients. We use a method derived from half-quadratic splitting (HQS) to handle the built MSBE model by decoupling it into simpler subproblems. Subsequently, these subproblems are solved by efficient and concise methods. In the MSBE phase, we also adopt a multiscale process to avoid local optima of the estimated BKs and adaptively update a penalty parameter to accelerate the iteration. In the ID phase, SotA restoration algorithms are employed by the proposed BID algorithm to obtain competitive final LIs. The experiments are conducted on several baseline datasets to evaluate our BID algorithm. Under the evaluation criteria of success-rate (SR), SSDE, PSNR, SSIM, vision effect, and running time (RT), our study shows apparent advantages compared with some SotA BID algorithms. These advantages include but are not limited to reducing the RT; obtaining higher PSNRs, SSIMs, and SRs; decreasing SSDEs; and obtaining high-quality LIs without artifacts. © (2024), (International Association of Engineers). All Rights Reserved.