Robust Blind Deconvolution Research Articles

Composite optimization for robust rank one bilinear sensing

Abstract We consider the task of recovering a pair of vectors from a set of rank one bilinear measurements, possibly corrupted by noise. Most notably, the problem of robust blind deconvolution can be modeled in this way. We consider a natural nonsmooth formulation of the rank one bilinear sensing problem and show that its moduli of weak convexity, sharpness and Lipschitz continuity are all dimension independent, under favorable statistical assumptions. This phenomenon persists even when up to half of the measurements are corrupted by noise. Consequently, standard algorithms, such as the subgradient and prox-linear methods, converge at a rapid dimension-independent rate when initialized within a constant relative error of the solution. We complete the paper with a new initialization strategy, complementing the local search algorithms. The initialization procedure is both provably efficient and robust to outlying measurements. Numerical experiments, on both simulated and real data, illustrate the developed theory and methods.

An effective variational model for simultaneous reconstruction and segmentation of blurred images

The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function κ is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

Robust penalty-weighted deblurring via kernel adaption using single image

Image blind deconvolution is well known as a challenging, ill-posed problem due to the uncertainty of the blur kernel and the noise condition. Based on our observations, blind deconvolution algorithms tend to generate disconnected and noisy blur kernels, which would yield a serious ringing effect in the restored image if the input image is noisy. Therefore, there is still room for further improvement, especially for noisy images captured under poor illumination conditions. In this paper, we propose a robust blind deconvolution algorithm by adopting a penalty-weighted anisotropic diffusion prior. On one hand, the anisotropic diffusion prior effectively eliminates the discontinuity in the blur kernel caused by the noisy input image during the process of kernel estimation. On the other hand, the weighted penalizer reduces the speckle noise of the blur kernel, thus improving the quality of the restored image. The effectiveness of the proposed algorithm is verified by both synthetic and real images with defocused or motion blur.

Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector

A robust blind deconvolution algorithm is proposed to cancel the sensor averaging effect caused by its wide detection area. The purpose herein is to retrieve features of the real signal that have been distorted by the averaging effect. The algorithm is applied to the case of an inductive loop detector. To perform the proposed algorithm, speed estimation is required. Vehicle reidentification rate from both raw signals and estimated real signals is compared. The sensor transfer function is calculated once from a learning phase; the estimated real signal is then computed in real time for the reidentification of each vehicle.

联合梯度预测与导引滤波的图像运动模糊复原

To modify the image motion blurring induced by the relative position motion between a camera and a subject,a robust blind deconvolution method based on single image motion blurring was proposed. Firstly,the strong edges of a latent image was predicted. By constraining image gradient,the point spread function in frequency domain was estimated fast and robustly by this method. After that,the non-blind deconvolution algorithm based on the gradient constrain was used to restore the latent image. Meanwhile,a new edge preserving filter-guided filter was used to suppress the ringing and noise. Experiments show that the proposed method can restore a high quality image with clear edges and tissues from single motion blurring image,and the operation time is less than 20 s.

Robust wavelet estimation and blind deconvolution of noisy surface seismics

Robust blind deconvolution is a challenging problem, particularly if the bandwidth of the seismic wavelet is narrow to very narrow; that is, if the wavelet bandwidth is similar to its principal frequency. The main problem is to estimate the phase of the wavelet with sufficient accuracy. The mutual information rate is a general-purpose criterion to measure whiteness using statistics of all orders. We modified this criterion to measure robustly the amplitude and phase spectrum of the wavelet in the presence of noise. No minimum phase assumptions were made. After wavelet estimation, we obtained an optimal deconvolution output using Wiener filtering. The new procedure performs well, even for very band-limited data; and it produces frequency-dependent phase estimates.