The Experts below are selected from a list of 1686 Experts worldwide ranked by ideXlab platform
Jan Kautz - One of the best experts on this subject based on the ideXlab platform.
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local Laplacian filters edge aware image processing with a Laplacian Pyramid
Communications of The ACM, 2015Co-Authors: Sylvain Paris, Samuel W Hasinoff, Jan KautzAbstract:The Laplacian Pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian Pyramid is widely believed to be ill-suited for representing edges, as well as for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, for example, anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to postprocess the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian Pyramids. We characterize edges with a simple threshold on pixel values that allow us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or postprocessing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos.
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Local Laplacian filters: edge-aware image processing with a Laplacian Pyramid
ACM Transactions on Graphics, 2011Co-Authors: Jan KautzAbstract:The Laplacian Pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian Pyramid is widely believed as being unable to represent edges well and as being ill-suited for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, e.g., anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to post-process the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian Pyramids. We characterize edges with a simple threshold on pixel values that allows us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or post-processing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos.
Sylvain Paris - One of the best experts on this subject based on the ideXlab platform.
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local Laplacian filters edge aware image processing with a Laplacian Pyramid
Communications of The ACM, 2015Co-Authors: Sylvain Paris, Samuel W Hasinoff, Jan KautzAbstract:The Laplacian Pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian Pyramid is widely believed to be ill-suited for representing edges, as well as for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, for example, anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to postprocess the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian Pyramids. We characterize edges with a simple threshold on pixel values that allow us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or postprocessing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos.
Fengbo Ren - One of the best experts on this subject based on the ideXlab platform.
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lapran a scalable Laplacian Pyramid reconstructive adversarial network for flexible compressive sensing reconstruction
European Conference on Computer Vision, 2018Co-Authors: Zhikang Zhang, Fengbo RenAbstract:This paper addresses the single-image compressive sensing (CS) and reconstruction problem. We propose a scalable Laplacian Pyramid reconstructive adversarial network (LAPRAN) that enables high-fidelity, flexible and fast CS images reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian Pyramid through multiple stages of reconstructive adversarial networks (RANs). At each Pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable Pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods. Experimental results on multiple public datasets show that LAPRAN offers an average 7.47 dB and 5.98 dB PSNR, and an average 57.93\(\%\) and 33.20\(\%\) SSIM improvement compared to model-based and data-driven baselines, respectively.
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lapran a scalable Laplacian Pyramid reconstructive adversarial network for flexible compressive sensing reconstruction
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Zhikang Zhang, Fengbo RenAbstract:This paper addresses the single-image compressive sensing (CS) and reconstruction problem. We propose a scalable Laplacian Pyramid reconstructive adversarial network (LAPRAN) that enables high-fidelity, flexible and fast CS images reconstruction. LAPRAN progressively reconstructs an image following the concept of Laplacian Pyramid through multiple stages of reconstructive adversarial networks (RANs). At each Pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable Pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods. Experimental results on multiple public datasets show that LAPRAN offers an average 7.47dB and 5.98dB PSNR, and an average 57.93% and 33.20% SSIM improvement compared to model-based and data-driven baselines, respectively.
Minghsuan Yang - One of the best experts on this subject based on the ideXlab platform.
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fast and accurate image super resolution with deep Laplacian Pyramid networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Co-Authors: Jiabin Huang, Narendra Ahuja, Minghsuan YangAbstract:Convolutional neural networks have recently demonstrated high-quality reconstruction for single image super-resolution. However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results. In this paper, we propose the deep Laplacian Pyramid Super-Resolution Network for fast and accurate image super-resolution. The proposed network progressively reconstructs the sub-band residuals of high-resolution images at multiple Pyramid levels. In contrast to existing methods that involve the bicubic interpolation for pre-processing (which results in large feature maps), the proposed method directly extracts features from the low-resolution input space and thereby entails low computational loads. We train the proposed network with deep supervision using the robust Charbonnier loss functions and achieve high-quality image reconstruction. Furthermore, we utilize the recursive layers to share parameters across as well as within Pyramid levels, and thus drastically reduce the number of parameters. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of run-time and image quality.
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deep Laplacian Pyramid networks for fast and accurate super resolution
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Weisheng Lai, Jiabin Huang, Narendra Ahuja, Minghsuan YangAbstract:Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each Pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.
Samuel W Hasinoff - One of the best experts on this subject based on the ideXlab platform.
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local Laplacian filters edge aware image processing with a Laplacian Pyramid
Communications of The ACM, 2015Co-Authors: Sylvain Paris, Samuel W Hasinoff, Jan KautzAbstract:The Laplacian Pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian Pyramid is widely believed to be ill-suited for representing edges, as well as for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, for example, anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to postprocess the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian Pyramids. We characterize edges with a simple threshold on pixel values that allow us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or postprocessing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos.
