Patent Publication Number: US-9406108-B2

Title: Deblurring of an image from a sequence of images

Description:
This application claims the benefit, under 35 U.S.C. §119 of European Patent Application 13305251.4, filed Mar. 6, 2013. 
     FIELD OF THE INVENTION 
     The invention relates to a method and an apparatus for deblurring an image from a sequence of images. More specifically, the invention relates to a method and an apparatus for reducing the image blur of fast moving objects captured by standard video cameras. 
     BACKGROUND OF THE INVENTION 
     In video processing motion blur often constitutes a problem, especially motion blur on fast moving objects. For example, for frame-rate up-conversion well-defined shapes, which are free from motion blur, are much easier to match across frames. This leads to better time-interpolated frames. Also for simulations of virtual cameras based on real images deblurred frames are important, as the original blur will typically not match the camera motion of the simulated virtual camera. 
     In S. Cho et al.: “ Removing Non - Uniform Motion Blur from Images ”, IEEE Int. Conf. on Computer Vision (ICCV) 2007, pp. 1-8, a method is presented for removing non-uniform motion blur from a set of blurred images. This method is parameterized by the number of possible motions within the image. Thus, prior to the removal of motion blur, the number of rigidly moving image regions must be known. Unfortunately, this is not reasonable in a real-world video deblurring scenario. 
     For cases where the image blur slowly varies across the image, e.g. when the observed scene motion is not fronto-parallel to the camera&#39;s image plane or the whole scene shows rigid motion with respect to the camera, a number of techniques are capable of obtaining accurate estimates of the slowly spatially varying Point Spread Function (PSF), even from just a single image. These techniques include the works of Q. Shan et al.: “ High - quality Motion Deblurring from a Single Image ”, ACM Transactions on Graphics (Proceedings of SIGGRAPH 2008), Vol. 27 (2008), pp. 73:1-73:10, O. Whyte et al.: “ Non - uniform Deblurring for Shaken Images ”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2010, pp. 491-498, M. Hirsch et al.: “ Fast Removal of Non - uniform Camera Shake ”, IEEE Int. Conf. on Computer Vision (ICCV) 2011, pp. 463-470, and L. Xu et al.: “ Depth - Aware Motion Deblurring ”, Int. Conf. on Computational Photography 2012, pp. 1-8. The latter shows the specificity that the estimation of the spatially varying PSF is driven by the knowledge about the scene&#39;s depth in stereo setups. 
     None of the approaches described in the above identified publications is capable of estimating the various and localized types of motion blur that can occur simultaneously when a camera with slow shutter speed captures events with fast motion, such as a sports event with players moving in a variety of directions with a fast speed. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to propose an improved solution for deblurring an image from a sequence of images. 
     According to the invention, a method for deblurring an image from a sequence of images, the image comprising pixels, comprises the steps of:
         estimating motion of the pixels of the image;   modeling point spread functions for the pixels of the image using the motion estimates;   determining a deblurred estimate of the image using the modeled point spread functions; and   iteratively improving the motion estimates for the pixels until the modeled point spread functions converge.       

     Accordingly, an apparatus for deblurring an image from a sequence of images, the image comprising pixels, comprises:
         a motion estimation stage for estimating motion of the pixels of the image;   a point spread function modeling stage for modeling point spread functions for the pixels of the image using the motion estimates;   a deconvolution stage for determining a deblurred estimate of the image using the modeled point spread functions; and   a feedback loop for iteratively improving the motion estimates for the pixels until the modeled point spread functions converge.       

     Also, a non-transitory computer readable storage medium has stored therein instructions enabling deblurring of an image from a sequence of images, the image comprising pixels, which when executed by a computer, cause the computer to:
         estimate motion of the pixels of the image;   model point spread functions for the pixels of the image using the motion estimates;   determine a deblurred estimate of the image using the modeled point spread functions; and   iteratively improve the motion estimates for the pixels until the modeled point spread functions converge.       

     The present invention proposes an improved solution for deblurring an image from a sequence of images. The motion of each pixel in each video frame in a video sequence is robustly estimated by block matching using the two temporally neighboring frames and by posterior filtering with a temporal coherence filter, a median filter, and a low-pass filter. The estimated motion is used for modeling the spatially varying PSF for each pixel. Using the PSFs a deblurred estimate is obtained from the input frame and the motion estimate of each pixel is improved, preferably by back-projecting the deblurred estimate of the image, until the spatially varying PSF estimate converges under a set of rules. 
     In contrast to existing techniques, the proposed solution is able to handle cases where an unknown or unlimited amount of motion blur artifacts in any direction and magnitude are present. The presented approach succeeds at retrieving a plausible estimate of the desired sharp image in video sequences with an undetermined number of independently moving objects. The resulting video frames, for which the (severe) motion blur has been corrected, appear sharper, with better defined contours around the moving objects, regardless of the number of motion directions causing blur in the captured scene. These deblurred images can then be used for further processing, e.g. to generate an appealing simulation of a real camera when navigating around a video panorama with a virtual camera. The synthesized blur due to the virtual camera&#39;s motion can be applied to objects moving in directions not parallel to the virtual camera motion, whereas the objects moving parallel to the camera motion are shown deblurred. The deblurred frames are also useful for frame-rate up-conversion and scan conversion. Well-defined shapes, with reduced motion blur, are easier to match across frames and lead to better time-interpolated frames. The solution according to the invention will typically be implemented in professional studio or post-processing equipment. However, it may likewise be implemented in consumer equipment, e.g. in a set-top box or as a video processing software running on a personal computer. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding the invention shall now be explained in more detail in the following description with reference to the figures. It is understood that the invention is not limited to this exemplary embodiment and that specified features can also expediently be combined and/or modified without departing from the scope of the present invention as defined in the appended claims. 
         FIG. 1  schematically illustrates a block diagram of an apparatus according to the invention; 
         FIG. 2  shows three frames of a short motion-blurred video sequence; 
         FIG. 3  shows overlays of a video frame and the grey value encoded estimated motion field; 
         FIG. 4  shows a block diagram of a motion estimation method used for implementing a method according to the invention, 
         FIG. 5  shows estimated motion fields output by the different stages of  FIG. 4 ; 
         FIG. 6  illustrates the effect of setting a magnitude scaling factor to different values; 
         FIG. 7  shows deconvolved images obtained by considering only motion blur respectively motion blur and lens blur in combination; 
         FIG. 8  visualizes an approach for motion and deblurring optimization based on back-projection test and model refinement with convergence detection; and 
         FIG. 9  shows an initial and a final deblurred image. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     Although in the following description the process of deblurring each frame is considered in a global manner, i.e. the whole image is treated as a single block, a possible alternative implementation would first split the frames into small overlapping blocks, proceed with the proposed method for each block, and then recompose the deblurred frame. 
     A schematic overview of an apparatus according to the invention for video deblurring is illustrated in  FIG. 1 . The basic composing blocks are a motion estimation stage  1 , where the apparent motion of each pixel is estimated, a PSF modeling stage  2 , where the estimated motion is used to generate an estimate of the spatially varying point spread function, a frame deconvolution stage  3 , where the estimated PSF is used for deconvolving the current frame, and a convergence detection stage  4 , where the deblurred frame is accepted when the PSF model converges. As mentioned, the proposed method is individually applied to each frame of a video sequence. The only stage at which the information of several frames is combined is the initial motion estimation stage  1 . 
     In the following a detailed descriptions of each involved stage shall be provided. 
     The main goal of the motion estimation stage  1  stage is to provide robust motion estimates for video frames with severe motion blur artifacts. It would seem logical to choose one of the top performing state-of-the-art approaches for optical flow estimation, like D. Sun et al.: “ Secrets of optical flow estimation and their principles ”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2010, pp. 2432-2439, in order to estimate the per-pixel motion of each video frame. However, this type of motion estimation method does not correctly handle severely blurred images. An example of such severely blurred images is depicted in  FIG. 2 , which shows three consecutive blurred input frames of a short motion-blurred video sequence. The fast moving leg of the player on the right side of the frames is severely blurred. The frames also illustrate that there are different degrees of blur related to objects with different speed relative to the camera&#39;s image plane. 
     For the above mentioned state-of-the-art optical flow estimation method data measurements are pixel-wise instead of block-wise. When the method is applied to the pairs of frames t−1 and t and t and t+1 of  FIG. 2 , the severe distortion caused by motion blur and the large degree of disparity of small independently moving elements, e.g. the fast moving leg with respect to both the background and the slower moving body, make this approach underestimate the speed of locally fast moving elements. 
       FIG. 3  shows an overlay of the grey value-encoded estimated motion field for the frame t and the original image for both the above mentioned state-of-the-art optical flow estimation method ( FIG. 3 a   )) and the method proposed in the following description ( FIG. 3 b   )). The darkest grey values should be found for the fastest moving parts, e.g. the fast moving leg of the player to the right. As can be seen, in case of the optical flow-based approach the darkest grey values are found for the upper body of the player to right. In contrast, in case of the method proposed herein the darkest grey values are found for the actual fastest moving part of the scene, i.e. the fast moving leg and foot. 
     The proposed approach uses a more robust data fidelity measure, namely block-matching, than the one considered in the optical flow approach. Furthermore, a number of post-processing stages are applied in order to better condition the estimated motion blur. The complete block diagram of the proposed motion estimation method is shown in  FIG. 4 . Motion estimation is based on three consecutive video frames. A block matching algorithm obtains the initial estimates for each pair of consecutive frames with two block matching stages  5 ,  6 . These are combined by averaging them. Then, a non-linear temporal coherence filter  7  removes non-parallel motion estimates, a spatial median filter  8  removes isolated outliers and a final low-pass filter  9  provides a smooth output motion field. 
     While the block diagram in  FIG. 4  already provides a sufficient description of most of the involved sub-stages, i.e. block matching  5 ,  6 , median filters  8  and low-pass filters  9  are well known, special attention has to be given to the temporal coherence filter  7 . As shown in  FIG. 4 , this non-linear filter receives the pre-flow −f 1 , the post-flow, and the combined flow 
             f   =           f   1     +     f   2       2     .           
Then, the response of this temporal filter, f 0 , is
 
                     f   0     =     {         0           〈       f   1     ,     f   2       〉     &lt;     τ   ⁡     (              f   1          2   2     +            f   2          2   2       )                 f             〈       f   1     ,     f   2       〉     ≥     τ   ⁡     (              f   1          2   2     +            f   2          2   2       )         ,                     (   1   )               
where τ is a threshold that sets the tolerance to dissimilarity between the two available motion estimates for each pixel and &lt;,&gt; is the dot product. When a time-consistent motion estimate cannot be obtained, the actual velocity might be non-constant, or the level of noise too high, so it is preferable not to account for these pixels and to set their estimated motion to zero.
 
       FIGS. 5 a   ) to  d ) show exemplary motion estimates after each post-processing stage, namely after block matching and averaging ( FIG. 5 a   )), after the temporal coherence filter ( FIG. 5 b   )), after the spatial median filter ( FIG. 5 c   )), and after the spatial low-pass filter ( FIG. 5 d   )). As can be seen, the temporal coherence filter shows the most dramatic influence on the improvement of the initial motion estimate. 
     A complete realization of the proposed motion estimation stage can be obtained by setting the block matching window size to 41 pixels, the temporal coherence tolerance threshold to ¼, the median filter window size to 5×5 pixels and the normalized cut-off frequency and size of the low-pass filter to ¼ and 16×16 pixels, respectively. These parameters were employed for obtaining the results illustrated in  FIG. 5 . 
     Concerning the PSF modeling stage  2 , the PSF corresponding to each pixel is modeled as a symmetric linear motion blur kernel, with the direction of the estimated motion vector and proportional magnitude. The only adjustable parameter is the scaling factor of the magnitude. This is closely related to the shutter time of the video camera. If the camera shutter is open during e.g. one quarter of the inverse of the frame-rate, the scaling factor must be set to this ratio between the shutter time and the total time slot for each frame. This corresponds to the assumption that motion is constant during the time corresponding to a frame, which is reasonable at high frame-rates. 
     In  FIG. 6  the effect of setting the scaling factor to different values is shown. It can be observed that, for the processed video sequence, the shutter appears to be open during the whole duration of the frame. Noteworthy are the plausibly deblurred moving elements when the parameter equals 1. 
     Additionally, also the lens blur can be modeled, even for regions where no motion is present. Typically, lens blur can be modeled as a disk kernel with radius ρ, where only the pixels inside the disk are set to one and the rest is set to zero, with pixels lying on the border of the disk having a weight proportional to the area of the disk they intersect. A comparison between the deblurred images is shown in  FIG. 7 .  FIG. 7 a   ) shows the deconvolved image obtained by considering only motion blur.  FIG. 7 b   ) shows the deconvolved image obtained considering both motion blur and added lens blur, with ρ=1. The model including lens blur results in an increased level of sharpness. Although slight variations across the image might exist, a spatially invariant kernel plausibly approximates the actual lens blur. Otherwise, techniques like the one presented in N. Joshi et al.: “ PSF Estimation using Sharp Edge Prediction ”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2008, pp. 1-8, may also be applied. 
     Regarding frame deconvolution, the image formation model
 
 y ( p )= h ( s,p )· b ( p )· x ( p )+ n ( p )  (2)
 
is considered, where h(s,p) is the spatially varying motion blur kernel, b(p) is the spatially invariant lens blur, x(p) is the latent sharp image to recover, and n(p) is independent and identically distributed additive white Gaussian noise.
 
     Then, using a matrix formulation with both images and noise vectorized by stacking them in a single column vector, this model can be expressed as y=HBx+n, where H and B are sparse square matrices. Given H and B, a least squares estimator of the latent sharp image x is 
                     x   ~     :=         arg   ⁢           ⁢   min     x     ⁢           ⁢              y   -   HBx          2   2     .               (   3   )               
Note that ∥x∥ p ≡ p √{square root over (Σ ∀i |x i | p )}, where {x i } are the elements of vector x. In order to improve the conditioning of the problem, as H and B may contain errors, a more robust strategy is followed, which introduces prior knowledge about the image formation model. This means that the latent image must be smooth but still contain sharp borders. Thus, the deconvolution process is reformulated as
 
                       x   ~     :=         arg   ⁢           ⁢   min     x     ⁢           ⁢     {           ⁢              y   -   HBx          2   2     +       λ   1     ⁢              Δ   ⁢           ⁢   x          2   2       ︸   smoothness         +       λ   2     ⁢              ∇   x          1       ︸     sharp   ⁢           ⁢   borders             }         ,           (   4   )               
where the smoothness prior is expressed as a Tikhonov regularizer with a second derivative (Laplacian) and the sharpness preserving term is an anisotropic Total Variation regularizer. The factors λ 1 , with typical values in the range from 10 −2  to 10 −1 , and λ 2 , with values between 10 −3  and 10 −2 , are the respective regularization weights.
 
     The chosen algorithm to solve this problem is a gradient descent approach with a predefined varying step size μ t . This step size is defined to be large during the first iterations, when the output image x is still far from the optimum, and small during the last iterations, when only small adjustments are necessary to converge at the optimal image. More specifically, possible initial) (μ 0 ) and final (μ f ) values for μ t  are 1 and 0.01. In the experiments, these values were used as initial and final step sizes during 500 iterations. The step size is updated as 
                       μ   t     :=           μ   f       μ   0           N   ite     -   1       ⁢     μ     t   -   1           ,           (   5   )               
where N ite  is the desired number of iterations. The gradient of the complete functional described above is computed as
 
∇ t =2 B   T   H   T   HBx   t −2 B   T   H   T   y+ 2λ 1   L   T   Lx   t +λ 2 ( D   u   T  sign( D   u   x   t )+ D   v   T  sign( D   v   x   t )),  (6)
 
where D u  and D v  are two linear operators performing the horizontal and vertical gradients (forward differences) and L is a linear operator performing the Laplacian (L=D u   T D u +D v   T D v ). The gradient is normalized before updating the output image. Note that a capital T denotes a matrix transposition operation, whereas a lowercase t denotes the current iteration. The resulting iteration is
 
                       x   ~       t   +   1       :=         x   ~     t     -       μ   t     ⁢         ∇   t              ∇   t          2       .                 (   7   )               
With this iteration, the magnitude of the variation in the estimated sharp image is entirely defined by the predefined update step μ t , whereas the direction of the applied changes is defined by the functional gradient. The output of this stage is the deblurred image x.
 
     When the deblurred image x is obtained, it is filtered with the blur matrices B and H (back-projection). Ideally, the resulting image would closely resemble the blurred image y, assuming a typically low level of additive noise. Due to modeling errors, some pixels in HBx largely deviate from the corresponding value in y. Therefore, the PSF modeling stage  2  and the frame deconvolution stage  3  are iteratively applied until iterative back-projection reaches convergence according to the following rules, which are applied in the convergence detection stage  4 :
     1. A tolerance threshold of 2 bit out of the total 8 bit per component is set, i.e. differences smaller than 4 are within the tolerance threshold and thus ignored.   2. When the difference affects the 3rd LSB (Least Significant Bit) and beyond, the pixel blur model&#39;s magnitude is reduced by one unit, if its magnitude is larger than or equal to two.   3. When all wrong pixel&#39;s motion blur magnitude is smaller than 2, or no pixel shows a difference above the tolerance threshold, the convergence detector outputs the deblurred image x and terminates the iterative back-projection.   

     Application of a full iterative back-projection scheme would suffer from the problem of not being able to generate a complete prediction of H at every stage, due to the sparse available amount of data. However, relaxing this to a heuristic behavior, built upon the assumption that errors are only due to overestimation of the pixel&#39;s motion magnitude, allows effectively improving the blur model without introducing further regularizers and complexity. 
     This is used in an iterative manner, as shown in  FIG. 8 . After an initial motion estimation  10  the PSF is modeled  11  based on the detected motion information. Then this model is used to deblur  12  the image. Back-projection is used to measure the errors. If tests  13 ,  14  yield that the errors are below the threshold or the model does not allow further reduction of the motion magnitude for pixels with errors, the method terminates and the deblurred image is output  16 . Otherwise, the motion&#39;s magnitude of each pixel is reduced  15  by one unit and the method goes back to the PSF modeling step  11 . 
     In practice a small number of iterations, e.g. 5 to 10 iteration, suffices to obtain a suitable deblurred image. In  FIG. 9 , the initial deblurred image ( FIG. 9 a   )) with severe back-projection errors and the final deblurred image ( FIG. 9 a   )) are shown, where convergence is reached after only five iterations. As can be seen, the PSF model refinement actually allows reducing the amount of artifacts around contours, where motion estimation is prone to include more errors.