Journal ArticleDOI
Resolution Enhancement in Multi-Image Stereo
Arnav Bhavsar,A. N. Rajagopalan +1 more
TLDR
This paper proposes an integrated approach to estimate the HR depth and the SR image from multiple LR stereo observations and demonstrates the efficacy of the proposed method in not only being able to bring out image details but also in enhancing theHR depth over its LR counterpart.Abstract:
Under stereo settings, the twin problems of image superresolution (SR) and high-resolution (HR) depth estimation are intertwined. The subpixel registration information required for image superresolution is tightly coupled to the 3D structure. The effects of parallax and pixel averaging (inherent in the downsampling process) preclude a priori estimation of pixel motion for superresolution. These factors also compound the correspondence problem at low resolution (LR), which in turn affects the quality of the LR depth estimates. In this paper, we propose an integrated approach to estimate the HR depth and the SR image from multiple LR stereo observations. Our results demonstrate the efficacy of the proposed method in not only being able to bring out image details but also in enhancing the HR depth over its LR counterpart.read more
Citations
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Posted Content
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
TL;DR: The Flickr1024 dataset as mentioned in this paper is a large-scale stereo dataset which contains 1024 pairs of high-quality images and covers diverse scenarios, which can help to handle the overfitting problem and significantly improves the performance of stereo SR methods.
Posted Content
Learning Parallax Attention for Stereo Image Super-Resolution
TL;DR: Zhang et al. as discussed by the authors proposed a parallax-attention stereo super-resolution network (PASSRnet) to integrate the information from a stereo image pair for SR.
Dissertation
Solving Multi-view Stereo and Image Restoration using a Unified Framework
TL;DR: Experiments show that the proposed method can restore high-quality depth maps from seriously degraded images for both synthetic and real video, as opposed to the failure of simple multi-view stereo methods and can be generalized to handle more common scenarios.
Proceedings Article
Joint multi-frame super-resolution and matting
TL;DR: A multi-frame approach is adopted which uses data from adjacent frames to increase the resolution of the matte as well as foreground in the super-resolution model.
References
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
Journal ArticleDOI
Fast approximate energy minimization via graph cuts
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Proceedings ArticleDOI
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
TL;DR: This paper first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties, then describes the process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduces the evaluation methodology.
Proceedings ArticleDOI
Computing visual correspondence with occlusions using graph cuts
Vladimir Kolmogorov,Ramin Zabih +1 more
TL;DR: This paper presents a new method which properly addresses occlusions, while preserving the advantages of graph cut algorithms, and gives experimental results for stereo as well as motion, which demonstrate that the method performs well both at detecting occlusion and computing disparities.
Book
Markov Random Field Modeling in Computer Vision
TL;DR: This book presents a comprehensive study on the use of MRFs for solving computer vision problems, and covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms.