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ON THE IMPROVEMENT OF IMAGE REGISTRATION FOR HIGH ACCURACY SUPER-RESOLUTION. Michalis Vrigkas , Christophoros Nikou , Lisimachos P. Kondi University of Ioannina Department of Computer Science Ioannina , Greece. MOTIVATION. Objective

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on the improvement of image registration for high accuracy super resolution
ON THE IMPROVEMENT OF IMAGE REGISTRATION FOR HIGH ACCURACY SUPER-RESOLUTIONMichalisVrigkas, ChristophorosNikou, Lisimachos P. KondiUniversity of IoanninaDepartment of Computer ScienceIoannina, Greece
motivation
MOTIVATION
  • Objective
    • Reconstruct a high-resolution image from a sequence of low-resolution images.
      • Improve spatial resolution.
  • Constraints on low-resolution images
    • Motion
    • Rotation
    • Blurring
    • Subsampling
    • Additive noise
approach
APPROACH
  • MAP scheme for image super-resolution.
  • Registration in two parts
    • At first, the LR images are registered by establishing correspondences between robust SIFT (Scale-Invariant Feature Transform) features.
    • In the second step, the estimation of the registration parameters is fine tuned along with the estimation of the HR image.
      • Mutual Information Criterion: maximize the mutual information between HR image and each of the upscaled LR images.
formulation model
FORMULATION MODEL
  • Let the high-resolution image

where

  • The set of LR images is described as

We consider p LR images each of size

formulation model cont
FORMULATION MODEL (cont.)
  • Observation model:
    • All images are ordered lexicographically
    • represents zero-mean additive Gaussian noise,
    • is the degradation matrix, performing the operations of:
      • motion
      • blur
      • down-sampling
map estimator
MAP ESTIMATOR
  • The Gaussian prior for the HR image is:
    • is the Laplacian of the image z
    • controls the precision and the shape of the distribution
  • The likelihood of the LR images is Gaussian:
map estimator cont
MAP ESTIMATOR (cont.)
  • MAP approach
    • Maximize
    • Which leads to a MAP functional to be minimized with respect to HR image z and the transformation parameters s:
  • Use gradient descent method
    • The update equation is given by:

where εn is the step size at the n-th iteration.

scale invariant featute transform sift
SCALE INVARIANT FEATUTE TRANSFORM - SIFT
  • Objective: independently detect corresponding keypoints in scaled versions of the same image.
  • Idea: Given a keypoint in two images, determine if the surrounding neighborhoods contain the same structure up to scale.
  • SIFT features are invariant to:
    • Image scale and rotation
    • Affine transformations
    • Changes in illumination and noise

[D. G. Lowe. "Distinctive image features from scale invariant keypoints.”International Journal of Computer Vision 60 (2), pp. 91-110, 2004.]

mutual information criterion
MUTUAL INFORMATION CRITERION
  • Basics: the mutual information is maximized when the two images are correctly registered.
  • The mutual information between two images A and B is:
    • H(A) and H(B) are the marginal entropies of the random variables A and B.
    • H(A,B)is the joint entropy.
mutual information criterion cont
MUTUAL INFORMATION CRITERION (cont.)
  • Normalized Mutual Information:
    • Robust measure in order to provide invariance to the overlapping areas between the two images.
  • Problem:
    • If mutual information is not initialized close to the optimal solution it is trapped by local maxima.
      • Good initialization is important.
  • Solution:
    • Initialization using SIFT descriptors.
image registration
Image Registration
  • Estimation of registration parameters in two steps.
    • First step, LR images are registered by employing SIFT features.
      • Minimization of mean square error between the locations of features between the reference image and the LR images.
      • Provides good initialization.
image registration cont
Image Registration (cont.)
    • Second step, the estimation of the registration parameters is fine-tuned along with the estimation of the HR image, by maximization of mutual information criterion.
      • Iterative scheme.
  • Contribution:
    • The registration accuracy is improved at each iteration step.
    • Refinement of the mutual information registration.
experimental parameters
EXPERIMENTAL PARAMETERS
  • Synthetic data sets.
  • LR images were created by rotating, translating, blurring, down-sampling and degrading by noise.
    • Translation: uniformly selected in [-3, 3] (in pixels)
    • Rotation: uniformly selected in [-5, 5] (in degrees)
    • Down-sampling factor: 2 (4 pixels to 1)
    • Blurring: 5x5 Gaussian kernel, standard deviation of 1
    • Additive noise: AWGN to obtain SNR of 30 dB and 20 dB
experimental parameters cont
EXPERIMENTAL PARAMETERS (cont.)
  • First estimate of the HR image
    • Bicubic interpolation
  • Total number of realizations for each case: 10
  • Convergence: or 70 iterations reached.
  • Quantitative evaluation: peak signal to noise ratio
experimetal results
EXPERIMETAL RESULTS
  • Books (PSNR = 26.06 dB)
  • 4 LR images used

LR image

Reconstructed HR image

experimetal results cont
EXPERIMETAL RESULTS (cont.)
  • Front page (PSNR = 26.14 dB)
  • 6 LR images used

LR image

Reconstructed HR image

experimetal results cont1
EXPERIMETAL RESULTS (cont.)
  • Car (PSNR = 28.13 dB)
  • 5 LR images used

LR image

Reconstructed HR image

experimetal results cont2
EXPERIMETAL RESULTS (cont.)
  • Eye chart (PSNR = 27.33 dB)
  • 4 LR images used

LR image

Reconstructed HR image

experimetal results cont3
EXPERIMETAL RESULTS (cont.)
  • Statistics for the compared SR methods

+1.5 dB on average better results than SIFT.

conclutions
CONCLUTIONS
  • Hybrid registration approach
    • SIFT-based image registration combined with the maximization of mutual information.
    • High precision registration
  • High accuracy super-resolved image.
    • Improvement is 1.5 dB on average higher for both 30 dB and 20 dB.
  • Proposed algorithm converges faster than the standard solution.