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How do you deal with geometric distortions and alignment issues in DIP?

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Geometric distortions and alignment issues are common challenges in digital image processing (DIP) that can affect the quality and accuracy of the final results. Whether you are working with medical images, satellite images, face recognition, or any other application that involves DIP, you need to know how to deal with these problems effectively. In this article, you will learn some basic concepts and techniques for image preprocessing, such as geometric transformations, image registration, and image stitching, that can help you correct or compensate for the distortions and misalignments in your images.

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Vasiliy Kulakov

Staff Software Engineer at LinkedIn
Shahin Youssefi

Software Developer@ WISI Group (Inca Networks) | C++ Audio/Video Encoder/Decoder Developer

1 Geometric transformations

A geometric transformation is a mathematical operation that changes the position, orientation, shape, or size of an image. Some common types of geometric transformations are translation, rotation, scaling, shearing, and perspective. You can use geometric transformations to adjust the image coordinates, align the image with a reference frame, or enhance the image features. For example, you can rotate an image to correct its orientation, scale an image to change its resolution, or apply a perspective transformation to create a 3D effect. To perform a geometric transformation, you need to define a transformation matrix that specifies how each pixel in the source image maps to a pixel in the destination image. You can use various tools and libraries, such as OpenCV, MATLAB, or scikit-image, to apply geometric transformations to your images.

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Vasiliy Kulakov

Staff Software Engineer at LinkedIn
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Geometry transformation doesn’t commonly map pixels — it’s only transforming the geometry (cube, rectangle, etc) onto which a texture/image may be mapped, and has little to do with discrete pixels. It’s just taking a set of points in some arbitrary coordinate space, and transforming them to a new set of points (which could be in a new coordinate space, such as the screen), usually via matrix operations. What you end up with are vertices in space. Mapping pixels is normally performed by a separate step, usually rasterization (a process where discrete output surface pixels are intersected with your resulting geometry comprised of the new vertices).

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2 Image registration

Image registration is the process of aligning two or more images of the same scene or object that are taken from different viewpoints, times, sensors, or modalities. Image registration is useful for combining or comparing multiple images, such as in medical imaging, remote sensing, or computer vision. For example, you can register MRI and CT images of the same patient to obtain a more complete and accurate diagnosis, or you can register aerial and satellite images of the same area to detect changes over time. To perform image registration, you need to find the correspondence between the features or pixels in the images, and then estimate the optimal transformation that minimizes the misalignment error. You can use various methods and algorithms, such as feature-based, intensity-based, or frequency-based, to achieve image registration.

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Shahin Youssefi

Software Developer@ WISI Group (Inca Networks) | C++ Audio/Video Encoder/Decoder Developer
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There are two main approaches for IR: "feature registration" and "intensity registration". Feature registration means that some elements are detected in both images, and a transformation is applied on the scene image such that selected elements match their correspondents in the object image. In intensity registration, we use all the pixels in the images during the registration process. Evolutionary image registration is a dynamic field and tests carried out on algorithms showed that while improvements are achieved in robustness, accuracy and time consumption, there are variations regarding the performance between datasets. A universally applicable algorithm combination is yet to be found, so the path is open for further exploration.

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3 Image stitching

Image stitching is the process of combining two or more images of the same scene or object that are partially overlapping to form a larger and more complete image. Image stitching is useful for creating panoramic images, such as in photography, virtual reality, or surveillance. For example, you can stitch multiple images of a landscape to create a wide-angle view, or you can stitch multiple images of a building to create a 360-degree view. To perform image stitching, you need to follow several steps, such as finding the keypoints and descriptors in the images, matching the keypoints and descriptors across the images, estimating the homography matrix that relates the images, warping the images according to the homography matrix, and blending the images to remove seams and artifacts. You can use various tools and libraries, such as OpenCV, MATLAB, or Hugin, to perform image stitching.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you deal with geometric distortions and alignment issues in DIP?

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1 Geometric transformations

2 Image registration

3 Image stitching

4 Here’s what else to consider

Digital Image Processing

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