Variational Bayesian Blind Image Deconvolution: A review <s> F. Implementation issues <s> Existing multichannel blind restoration techniques assume perfect spatial alignment of channels, correct estimation of blur size, and are prone to noise. We developed an alternating minimization scheme based on a maximum a posteriori estimation with a priori distribution of blurs derived from the multichannel framework and a priori distribution of original images defined by the variational integral. This stochastic approach enables us to recover the blurs and the original image from channels severely corrupted by noise. We observe that the exact knowledge of the blur size is not necessary, and we prove that translation misregistration up to a certain extent can be automatically removed in the restoration process. <s> BIB001 </s> Variational Bayesian Blind Image Deconvolution: A review <s> F. Implementation issues <s> A conventional camera captures blurred versions of scene information away from the plane of focus. Camera systems have been proposed that allow for recording all-focus images, or for extracting depth, but to record both simultaneously has required more extensive hardware and reduced spatial resolution. We propose a simple modification to a conventional camera that allows for the simultaneous recovery of both (a) high resolution image information and (b) depth information adequate for semi-automatic extraction of a layered depth representation of the image. ::: Our modification is to insert a patterned occluder within the aperture of the camera lens, creating a coded aperture. We introduce a criterion for depth discriminability which we use to design the preferred aperture pattern. Using a statistical model of images, we can recover both depth information and an all-focus image from single photographs taken with the modified camera. A layered depth map is then extracted, requiring user-drawn strokes to clarify layer assignments in some cases. The resulting sharp image and layered depth map can be combined for various photographic applications, including automatic scene segmentation, post-exposure refocusing, or re-rendering of the scene from an alternate viewpoint. <s> BIB002 </s> Variational Bayesian Blind Image Deconvolution: A review <s> F. Implementation issues <s> Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. On the other hand we show that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. We have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrates that the shift-invariant blur assumption made by most algorithms is often violated. <s> BIB003 </s> Variational Bayesian Blind Image Deconvolution: A review <s> F. Implementation issues <s> Advanced image processing algorithms usually require high computing performance. Today's personal computers (PCs) offer satisfying resources for implementation of image processing tasks. However, as the image processing techniques are becoming more and more complex other implementation possibilities have to be searched. Since image processing algorithms usually comply with the Single Instruction Multiple Data (SIMD) model, implementation efforts using such hardware resources are suitable. An example of the SIMD hardware component available nowadays is the graphics processor (GPU) embedded in modern graphics cards manufactured for PCs. In this paper, the implementation of a blind image deconvolution algorithm using graphics processor as the SIMD computing resource is presented. The resulting performance is compared to the performance achieved on a common general-purpose processor (CPU). <s> BIB004 </s> Variational Bayesian Blind Image Deconvolution: A review <s> F. Implementation issues <s> Camera shake leads to non-uniform image blurs. State-of-the-art methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this paper we develop a forward model based on the efficient filter flow framework, incorporating the particularities of camera shake, and show how an efficient algorithm for blur removal can be obtained. Comprehensive comparisons on a number of real-world blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results. <s> BIB005 </s> Variational Bayesian Blind Image Deconvolution: A review <s> F. Implementation issues <s> In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k\y) and not only its mode. This leads to a distinction between MAP x, k strategies which estimate the mode pair x, k and often lead to undesired results, and MAP k strategies which select the best k while marginalizing over all possible x images. The MAP k principle is significantly more robust than the MAP x, k one, yet, it involves a challenging marginalization over latent images. As a result, MAP k techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAP k algorithm which involves only a modest modification of common MAP x, k algorithms. We show that MAP k can, in fact, be optimized easily, with no additional computational complexity. <s> BIB006
Since BID methods need to estimate both image and blur, they usually take a significant amount of time. Apart from developing mathematically efficient methods to compute blur and image estimates, efficient implementations are needed to speed up the algorithms. Nowadays, most computers are equipped with graphical processing units (GPUs) that have several GFLOPS of computing power. Massive computing using these GPU or hybrid CPU+GPU computing can dramatically improve the speed of the algorithms. Most of the deconvolution implementations using GPUs are based on their capability to accelerate an FFT, with the CUDA framework and the CUFFT library  being the most popular implementation. Some BID methods have been implemented using GPUs with great success as proved in BIB004 where the time needed to blindly deconvolve an 8 MPixel image using the method in BIB001 is reduced from 55.6s to 13.8s. The EFF spatially variant blind image deconvolution method in BIB005 runs about ten times faster using GPU than using only CPU. Several efforts have also been carried out to use GPU computing in non-blind image deconvolution. For instance, Zhang et al. . We choose the widely used dataset BIB003 which consists of 32 images generated by 4 groundtruth images with 8 motion blurs. For the priors log, 0.8 , MOG, TV and bu3, we set α γ to 1, 10, 1, 20 and 0.1 respectively. After obtaining the kernels, we use the non-blind deconvolution method BIB002 with the same parameters used in BIB006 to reconstruct the final image. in the sense of error ratio metric (ratio between sum of squared difference errors of the restoration with the estimated kernel and the restoration with the groundtruth kernel, see BIB003 for more details). As we can see, the log prior has the best performance, with over 80% good restorations (error ratio ≤ 2) and 90% successful restorations (error ratio ≤ 3 is regarded as successful restoration, according to Levin et al. BIB006 ), followed by bu and MOG. 0.8 and TV also have good performance with about 80% and 70% successful restorations. It should be emphasized that, a suitable α γ is crucial for the different priors to work well. Fig. 4 shows some selected results for visual evaluation. 