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- Martin VetterliSuggested from co-authorship
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Works343 from public data
- 3,951
- 2,063
- Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance1,319
A statistical view of the texture retrieval problem is presented by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme that leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD).
- Semantic Image Inpainting with Deep Generative Models1,249
A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.
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- A Multi-Organ Nucleus Segmentation Challenge558
Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics as well as heavy data augmentation in the MoNuSeg 2018 challenge.
- Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train473
A novel approach to tensor completion, which recovers missing entries of data represented by tensors based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme.
- 469
- Directional multiresolution image representations383
This thesis focuses on the development of new "true" two-dimensional representations for images using a discrete framework that can lead to algorithmic implementations and a new family of block directional and orthonormal transforms based on the ridgelet idea.
- Semantic Image Inpainting with Perceptual and Contextual Losses.372
A novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network that can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods.
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- Framing pyramids317
It is shown that the LP with orthogonal filters is a tight frame, and thus, the optimal linear reconstruction using the dual frame operator has a simple structure that is symmetric with the forward transform.
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- Robust Image and Video Dehazing with Visual Artifact Suppression via Gradient Residual Minimization249
A new method for reliable suppression of different types of visual artifacts in image and video dehazing is proposed and can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips.
- Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models240
A statistical model for characterizing texture images based on wavelet-domain hidden Markov models that can be easily steered to characterize that texture at any other orientation and obtains a rotation-invariant model of the texture image.
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- SEAGULL: Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching225
A novel structure-preserving warping method is introduced to preserve salient curve and line structures during the warping to substantially improve the effectiveness of this method in dealing with a wide range of challenging images with large parallax.
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- Bayesian Blind Deconvolution with General Sparse Image Priors200
A general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors is presented and theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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