Abstract
In this work, we present a network architecture with parallel convolutional neural networks (CNN) for removing perspective distortion in images. While other works generate corrected images through the use of generative adversarial networks or encoder-decoder networks, we propose a method wherein three CNNs are trained in parallel, to predict a certain element pair in the transformation matrix, . The corrected image is produced by transforming the distorted input image using . The networks are trained from our generated distorted image dataset using KITTI images. Experimental results show promise in this approach, as our method is capable of correcting perspective distortions on images and outperforms other state-of-the-art methods. Our method also recovers the intended scale and proportion of the image, which is not observed in other works.
Highlights
Perspective distortion occurs if the objects in an image significantly differ in terms of scale and position, from how the objects are perceived by an observer [1]
Distorted images affect the visual perception of objects in the scene and perspective distortion correction is required on some aspects of photography and computer vision applications
We propose a framework for correcting first-order distortions using multiple convolutional neural networks trained in parallel, that compose the transformation matrix, Mof size 3 × 3, of a distorted image, where M is the ground-truth that caused the distortion (Figure 1)
Summary
Perspective distortion occurs if the objects in an image significantly differ in terms of scale and position, from how the objects are perceived by an observer [1]. This can be classified as first-order distortions modeled by multiplying an undistorted image with a transformation matrix M of size 3 × 3. First-order distortions can be caused by an incorrect acquisition environment, such as capturing from an incorrect angle or motions of objects or the photographer. Higher-order distortions are typically caused by capturing a scene with an inappropriate focal length. Distorted images affect the visual perception of objects in the scene and perspective distortion correction is required on some aspects of photography and computer vision applications
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