skip to main content


Title: Rigid registration algorithm based on the minimization of the total variation of the difference map
Image registration is broadly used in various scenarios in which similar scenes in different images are to be aligned. However, image registration becomes challenging when the contrasts and backgrounds in the images are vastly different. This work proposes using the total variation of the difference map between two images (TVDM) as a dissimilarity metric in rigid registration. A method based on TVDM minimization is implemented for image rigid registration. The method is tested with both synthesized and real experimental data that have various noise and background conditions. The performance of the proposed method is compared with the results of other rigid registration methods. It is demonstrated that the proposed method is highly accurate and robust and outperforms other methods in all of the tests. The new algorithm provides a robust option for image registrations that are critical to many nano-scale X-ray imaging and microscopy applications.  more » « less
Award ID(s):
1832613
NSF-PAR ID:
10353769
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of Synchrotron Radiation
Volume:
29
Issue:
4
ISSN:
1600-5775
Page Range / eLocation ID:
1085 to 1094
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Combining a hyperspectral (HS) image and a multi-spectral (MS) image---an example of image fusion---can result in a spatially and spectrally high-resolution image. Despite the plethora of fusion algorithms in remote sensing, a necessary prerequisite, namely registration, is mostly ignored. This limits their application to well-registered images from the same source. In this article, we propose and validate an integrated registration and fusion approach (code available at https://github.com/zhouyuanzxcv/Hyperspectral). The registration algorithm minimizes a least-squares (LSQ) objective function with the point spread function (PSF) incorporated together with a nonrigid freeform transformation applied to the HS image and a rigid transformation applied to the MS image. It can handle images with significant scale differences and spatial distortion. The fusion algorithm takes the full high-resolution HS image as an unknown in the objective function. Assuming that the pixels lie on a low-dimensional manifold invariant to local linear transformations from spectral degradation, the fusion optimization problem leads to a closed-form solution. The method was validated on the Pavia University, Salton Sea, and the Mississippi Gulfport datasets. When the proposed registration algorithm is compared to its rigid variant and two mutual information-based methods, it has the best accuracy for both the nonrigid simulated dataset and the real dataset, with an average error less than 0.15 pixels for nonrigid distortion of maximum 1 HS pixel. When the fusion algorithm is compared with current state-of-the-art algorithms, it has the best performance on images with registration errors as well as on simulations that do not consider registration effects. 
    more » « less
  2. Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration. 
    more » « less
  3. This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects of appearance changing areas. In particular, we develop a piecewise regularization on the tangent space of diffeomorphic transformations (also known as initial velocity fields) via learned segmentation maps of abnormal regions. The geometric transformation and appearance changes are treated as joint tasks that are mutually beneficial. Our model MetaMorph is more robust and accurate when searching for an optimal registration solution under the guidance of segmentation, which in turn improves the segmentation performance by providing appropriately augmented training labels. We validate MetaMorph on real 3D human brain tumor magnetic resonance imaging (MRI) scans. Experimental results show that our model outperforms the state-of-the-art learning-based registration models. The proposed MetaMorph has great potential in various image-guided clinical interventions, e.g., real-time image-guided navigation systems for tumor removal surgery. 
    more » « less
  4. There have been great advances in bridge inspection damage detection involving the use of deep learning models. However, automated detection models currently fall short of giving an inspector an understanding of how the damage has progressed from one inspection to the next. The rate-of-change of the damage is a critical piece of information used by engineers to determine appropriate maintenance and rehabilitation actions to prevent structural failures. We propose a simple methodology for registering two bridge inspection videos or still images, collected at different stages of deterioration, so that trained model predictions may be directly measured and damage progression compared. The changes may be documented and presented to the inspector so that they may quickly evaluate key interest regions in the inspection video or image. Three approaches referred to as rigid, deformable, and hybrid image registration methods were experimentally tested and evaluated based on their ability to preserve the geometric characteristics of the referenced image. It was found in all experiments that the rigid, homography-based transformations performed the best for this application over a state-of-the-art deformable registration method, RANSAC-Flow.

     
    more » « less
  5. This paper presents a tool-pose-informed variable center morphological polar transform to enhance segmentation of endoscopic images. The representation, while not loss-less, transforms rigid tool shapes into morphologies consistently more rectangular that may be more amenable to image segmentation networks. The proposed method was evaluated using the U-Net convolutional neural network, and the input images from endoscopy were represented in one of the four different coordinate formats (1) the original rectangular image representation, (2) the morphological polar coordinate transform, (3) the proposed variable center transform about the tool-tip pixel and (4) the proposed variable center transform about the tool vanishing point pixel. Previous work relied on the observations that endoscopic images typically exhibit unused border regions with content in the shape of a circle (since the image sensor is designed to be larger than the image circle to maximize available visual information in the constrained environment) and that the region of interest (ROI) was most ideally near the endoscopic image center. That work sought an intelligent method for, given an input image, carefully selecting between methods (1) and (2) for best image segmentation prediction. In this extension, the image center reference constraint for polar transformation in method (2) is relaxed via the development of a variable center morphological transformation. Transform center selection leads to different spatial distributions of image loss, and the transform-center location can be informed by robot kinematic model and endoscopic image data. In particular, this work is examined using the tool-tip and tool vanishing point on the image plane as candidate centers. The experiments were conducted for each of the four image representations using a data set of 8360 endoscopic images from real sinus surgery. The segmentation performance was evaluated with standard metrics, and some insight about loss and tool location effects on performance are provided. Overall, the results are promising, showing that selecting a transform center based on tool shape features using the proposed method can improve segmentation performance.

     
    more » « less