Spatially-varying out-of-focus image deblurring with L1-2 optimization and a guided blur map

Abstract

In this paper, we propose a spatially-varying deblurring method to remove the out-of-focus blur. Our proposed method mainly contains three parts: blur map generation, image deblurring, and scale selection. First, we derive a blur map using local contrast prior and the guided filter. Second, we propose our image deblurring method with L1-2 optimization to obtain a better image quality. Finally, we adopt the scale selection to ensure our output free from ringing artifacts. The experimental results demonstrate our proposed method is promising.Index Terms-out-of-focus blur, spatially-varying deblurring, L1-2 optimization, guided blur map.

Convolutional Deblurring for Natural Imaging

“…Trained CNN model to classify blur vs clean as image prior for regularized minimization formulation Simoes [35] 2016 NB • Diagonalizing unknown convolution operator using FFT and solving via ADMM Kim [36] 2015 B • • Encode temporal/spatial coherency of dynamic scene using optical-flow/TV regularized minimization Liu [37] 2014 B • • • Estimate blur from image spectral property and feed into a regularized TV/eigenvalue minimization Mosleh [38] 2014 N • • • Encode ringing artifacts using Gabor wavelets and fit into a regularized minimization for cancelation Pan [39] 2014 N • • • Text image deblurring regularized by sparse encoding of spatial/gradient domains Pan [40] 2013 B • • Estimates the kernel and the deblurred image from a combined sparse regularization framework Kim [41] 2013 B • • • Dynamic image deconvolution using TV/Tikhonov/temporal-sparsity regularized minimization Shen [42] 2012 B • • • TV/Tikhonov regularized minimization for image deconvolution Sroubek [43] 2012 B • • 1-regularized minimization for image deconvolution Dong [44] 2011 NB • • Learn adaptive bases and use in adaptive regularized minimization for sparse reconstruction Zhang [45], [46] 2011 B • • • Sparse regulation of images via KSVD library for deconvolution and apply to facial recognition Bai [47] 2018 B • • Both kernel/image recovered via combined regularization using reweighted graph TV priors Lou [48] 2015 N • • Weighted differences of TV regularizers in 1/ 2 norms and solved by split variable technique Zhang [49] 2014 N • • • Local/non-local similarities defined by TV 1 /TV 2 and regulated by combined minimization Xu [50] 2012 B • Regulate motion by difference of depth map and deconvolve via non-convex TV minimization Chan [51] 2011 N • Deconvolve image/videos using spatial/temporal TV regularization solved by split varying technique Afonso [52] 2010 N • Deconvolve image using TV regularization solved by split varying technique Li [53] 2018 B • • Non-iterative deconvolution via combination of Wiener filters, solution by a system linear equations Bertero [54] 2010 N • Generalized Kullback-Leiblar divergence function to regularize Poisson images Cho [16] 2009 B • • Separate recovery of motion kernel and image from residual image using Tikhonov regularization Wiener [55], [56] 1949 N • Regulate image spectrum in Fourier domain with inverse kernel response Xiao …”

Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

“…In Figure 13, we use our estimated blur map ( Figure 13(b)) in the deblurring algorithm described in [25] and recover the clear image; Figure 13(c) represents the deblurring result.…”

Saliency detection using superpixel belief propagation

“…is the texture smoothing approach [24] to remove the texture of the input image, and σ(x, y) is the defocus level extracted from the modified local contrast prior [25,26,27]. λ t and λ b are set to 10.…”