Summary Implicit Neural Image Stitching With Enhanced Feature Reconstruction arxiv.org
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Researchers from DGIST and Korea University have developed Implicit Neural Image Stitching (NIS), a technique that improves image quality by solving color mismatches and misalignment, with potential applications in panoramic images.
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Slide Presentation (10 slides)
Key Points
- Researchers proposed a novel approach called Implicit Neural Image Stitching (NIS) to address the limitations of existing image stitching methods.
- NIS extends arbitrary-scale super-resolution and estimates Fourier coefficients of images for quality-enhancing warps.
- NIS achieved improvement in resolving low-definition imaging compared to previous deep image stitching methods.
- NIS consists of three main components: a neural warping module, a blender, and a decoding INR.
- The researchers used synthetic and real datasets for training and evaluation, focusing on enhancing image details and improving feature blending.
- Quantitative evaluations showed that NIS outperformed other methods in terms of image quality metrics such as mPSNR and mSSIM.
- NIS has potential applications in various fields that require panoramic images, such as autonomous driving, virtual reality, and medical imaging.
Summaries
26 word summary
DGIST and Korea University researchers developed Implicit Neural Image Stitching (NIS), improving image quality by addressing color mismatches and misalignment. NIS has potential for panoramic images.
57 word summary
Researchers from DGIST and Korea University have developed Implicit Neural Image Stitching (NIS) to overcome limitations of traditional methods. NIS extends super-resolution and enhances image quality through Fourier coefficients. It blends color mismatches and misalignment in the latent space, decoding features into RGB values. NIS shows improvement in low-definition imaging and has potential applications in panoramic images.
134 word summary
Researchers from DGIST and Korea University have developed Implicit Neural Image Stitching (NIS), a new approach to overcome the limitations of traditional image stitching methods. NIS extends arbitrary-scale super-resolution and estimates Fourier coefficients to enhance image quality. It blends color mismatches and misalignment in the latent space and decodes features into RGB values of stitched images. NIS consists of a neural warping module, a blender, and a decoding INR. The researchers conducted experiments comparing NIS to other methods and found that NIS achieved improvement in resolving low-definition imaging. They also discussed related work and provided details on the training strategy and configurations of NIS. The experiments demonstrated the effectiveness of NIS in improving image stitching performance, with significant improvements in image quality and resolution. NIS has potential applications in various fields requiring panoramic images.
430 word summary
Researchers from DGIST and Korea University have developed a new approach called Implicit Neural Image Stitching (NIS) to overcome the limitations of traditional image stitching methods. These methods often result in blurry artifacts and disparities in illumination and depth level. Recent learning-based approaches have relaxed some of these disparities but sacrifice image quality and fail to capture high-frequency details. NIS extends arbitrary-scale super-resolution and estimates Fourier coefficients of images to enhance image quality. The model blends color mismatches and misalignment in the latent space and decodes the features into RGB values of stitched images.
Image stitching is used in various fields such as autonomous driving, virtual reality, and medical imaging to generate panoramas from multiple scenes. The researchers focused on view-fixed approaches and proposed NIS as a trainable stitching method with a neural network.
NIS consists of three main components: a neural warping module, a blender, and a decoding INR. The neural warping module estimates high-frequency-aware 2D features and aligns them according to given grids. The blender merges the warped features into a feature map, and the decoding INR predicts RGB values for each coordinate of the stitched image domain.
The researchers conducted experiments to evaluate the performance of NIS compared to other image stitching methods. NIS achieved improvement in resolving low-definition imaging compared to previous deep image stitching methods. The researchers also discussed related work, including homography estimation, implicit neural representation, image blending, and different approaches to image stitching.
The researchers provided details on the training strategy and configurations of NIS. They used synthetic and real datasets for training and evaluation. In the first stage of training, they focused on enhancing image details using synthetic data. In the second stage, they fine-tuned the model using real data to improve feature blending. They used a deep homography estimator and robust ELA for alignment estimation during training and testing.
The experiments demonstrated the effectiveness of NIS in improving image stitching performance. Quantitative evaluations showed that NIS outperformed other methods in terms of image quality metrics such as mPSNR and mSSIM. Qualitative comparisons also showed that NIS produced visually pleasing stitched images with enhanced details compared to other methods. The researchers also conducted an ablation study to analyze the contributions of different components of NIS.
In conclusion, NIS is a novel approach to implicit neural image stitching that addresses the limitations of existing methods. It combines arbitrary-scale super-resolution, Fourier coefficients estimation, and blending techniques to improve image quality. The experiments showed significant improvements in image quality and resolution of low-definition imaging. NIS has potential applications in various fields that require panoramic images.
632 word summary
Researchers from DGIST and Korea University have proposed a novel approach called Implicit Neural Image Stitching (NIS) that addresses the limitations of existing image stitching methods. Traditional frameworks for image stitching often result in visually reasonable stitchings but suffer from blurry artifacts and disparities in illumination, depth level, and other factors. Although recent learning-based stitchings have relaxed some of these disparities, they sacrifice image quality and fail to capture high-frequency details. To overcome these challenges, the researchers developed NIS, which extends arbitrary-scale super-resolution and estimates Fourier coefficients of images for quality-enhancing warps. The model blends color mismatches and misalignment in the latent space and decodes the features into RGB values of stitched images.
Image stitching is the process of generating a wider field-of-view panorama from multiple scenes with arbitrary views. It is used in various fields such as autonomous driving, virtual reality, and medical imaging. There are two categories of image stitching methods: view-fixed and view-free. View-fixed methods use pre-defined grid transformations to stitch different views, while view-free methods align multiple views without prior knowledge of the inter-relationship between scenes. The researchers focused on view-fixed approaches and proposed NIS as a trainable stitching method with a neural network.
NIS consists of three main components: a neural warping module, a blender, and a decoding INR. The neural warping module estimates high-frequency-aware 2D features and aligns them according to the given grids. The blender merges the warped features into a feature map, and the decoding INR predicts RGB values at each coordinate of the stitched image domain. The researchers conducted experiments to evaluate the performance of NIS compared to other image stitching methods. They found that NIS achieved improvement in resolving low-definition imaging compared to previous deep image stitching methods.
In terms of related work, the researchers discussed homography estimation, implicit neural representation, image blending, and different approaches to image stitching. Homography estimation involves estimating the transformation between two images based on feature correspondences. Deep homography estimators have been proposed to improve the accuracy of homography estimation. Implicit neural representation approximates continuous signals such as images and shapes. It has been applied to various tasks, including arbitrary image super-resolution and view synthesis. Image blending techniques combine overlapping regions of semantically aligned images to create a seamless result. Different methods such as gradient-domain smoothing, alpha blending, multi-band blending, and deep blending have been proposed for image blending. Finally, the researchers discussed different approaches to image stitching, including view-fixed and view-free methods that estimate geometric relations between images.
The researchers also provided details on the training strategy and configurations of NIS. They used synthetic and real datasets for training and evaluation. In the first stage of training, they focused on enhancing image details using synthetic data. In the second stage, they fine-tuned the model using real data to improve the blending of features. They used a deep homography estimator and robust ELA for alignment estimation during training and testing.
The experiments conducted by the researchers demonstrated the effectiveness of NIS in improving image stitching performance. Quantitative evaluations showed that NIS outperformed other methods in terms of image quality metrics such as mPSNR and mSSIM. Qualitative comparisons also showed that NIS produced visually pleasing stitched images with enhanced details compared to other methods. The researchers also conducted an ablation study to analyze the contributions of different components of NIS.
In conclusion, the researchers proposed NIS as a novel approach to implicit neural image stitching. The method addressed the limitations of existing image stitching methods by combining arbitrary-scale super-resolution, Fourier coefficients estimation, and blending techniques. The experiments showed that NIS achieved significant improvements in image quality and resolved low-definition imaging compared to previous methods. The researchers believe that NIS has potential applications in various fields that require panoramic images, such as autonomous driving, virtual reality, and medical imaging.