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Depth Image Enhancement Using Local Tangent Plane Approximations
"... This paper describes a depth image enhancement method for consumer RGB-D cameras. Most existing meth-ods use the pixel-coordinates of the aligned color image. Because the image plane generally has no relationship to the measured surfaces, the global coordinate system is not suitable to handle their ..."
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This paper describes a depth image enhancement method for consumer RGB-D cameras. Most existing meth-ods use the pixel-coordinates of the aligned color image. Because the image plane generally has no relationship to the measured surfaces, the global coordinate system is not suitable to handle their local geometries. To improve en-hancement accuracy, we use local tangent planes as local coordinates for the measured surfaces. Our method is com-posed of two steps, a calculation of the local tangents and surface reconstruction. To accurately estimate the local tangents, we propose a color heuristic calculation and an orientation correction using their positional relationships. Additionally, we propose a surface reconstruction method by ray-tracing to local tangents. In our method, accurate depth image enhancement is achieved by using the local ge-ometries approximated by the local tangents. We demonstrate the effectiveness of our method using synthetic and real sensor data. Our method has a high com-pletion rate and achieves the lowest errors in noisy cases when compared with existing techniques. 1.
Robust Image Filtering Using Joint Static and Dynamic Guidance
"... Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint up-sampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered struc-tures. One ..."
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Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint up-sampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered struc-tures. One of main drawbacks in such a data-dependent framework is that it does not handle differences in structure between guidance and input images. We address this prob-lem by jointly leveraging structural information of guid-ance and input images. Image filtering is formulated as a nonconvex optimization problem, which is solved by the majorization-minimization algorithm. The proposed algo-rithm converges quickly while guaranteeing a local mini-mum. It effectively controls image structures at different scales and can handle a variety of types of data from differ-ent sensors. We demonstrate the flexibility and effectiveness of our model in several applications including depth super-resolution, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.
SIRF: Simultaneous Satellite Image Registration and Fusion in A Unified Framework
"... Abstract—In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares f ..."
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Abstract—In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regular-izer. The former is to preserve accurate spectral information of the multispectral image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against six state-of-the-art image fusion methods on multispectral image datasets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world IKONOS datasets. Finally, a MATLAB implementation is provided to facilitate future research. Index Terms—Image fusion, pan-sharpening, image regis-tration, dynamic gradient sparsity, group sparsity, joint fusion I.
Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model
"... Abstract — This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a m ..."
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Abstract — This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative evaluation compared with ten state-of-the-art schemes show the effectiveness and superiority of our method. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, time-of-flight camera, and Kinect, as demonstrated by experiments on real systems. Index Terms — Depth recovery (upsampling, inpainting, denois-ing), autoregressive model, RGB-D camera.
JOURNAL OF LATEX CLASS FILES 1 SIRF: Simultaneous Image Registration and Fusion in A Unified Framework
"... Abstract—In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares f ..."
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Abstract—In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the multispectral image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against seven state-of-the-art image fusion methods on multispectral image datasets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world datasets. Finally, a MATLAB implementation is provided to facilitate future research. Index Terms—Image fusion, pan-sharpening, image registration, dynamic gradient sparsity, group sparsity, joint fusion F 1
