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Single image super-resolution from transformed self-exemplars

This paper expands the internal patch search space by allowing geometric variations, and proposes a compositional model to simultaneously handle both types of transformations to accommodate local shape variations.
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Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

This paper proposes the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images and generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications.
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Detecting Faces in Images: A Survey

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and…
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Robust visual tracking via multi-task sparse learning

Experimental results show that MTT methods consistently outperform state-of-the-art trackers and mining the interdependencies between particles improves tracking performance and overall computational complexity.
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A Comparative Study for Single Image Blind Deblurring

The first comprehensive perceptual study and analysis of single image blind deblurring using real-world blurred images and the correlation between human subjective scores and several full-reference and noreference image quality metrics is studied.
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Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks

This paper proposes the deep Laplacian Pyramid Super-Resolution Network for fast and accurate image super-resolution, and utilizes the recursive layers to share parameters across as well as within pyramid levels, and thus drastically reduce the number of parameters.
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Deep Joint Image Filtering

This paper proposes a learning-based approach to construct a joint filter based on Convolutional Neural Networks that can selectively transfer salient structures that are consistent in both guidance and target images and validate the effectiveness of the proposed joint filter through extensive comparisons with state-of-the-art methods.
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Gross motion planning—a survey

This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments.
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A fast scheme for image size change in the compressed domain

  • R. DugadN. Ahuja
  • Computer Science
    IEEE Trans. Circuits Syst. Video Technol.
  • 1 April 2001
An algorithm for downsampling and also upsampling in the compressed domain which is computationally much faster, produces visually sharper images, and gives significant improvements in PSNR (typically 4-dB better compared to bilinear interpolation).
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Maximum Margin Distance Learning for Dynamic Texture Recognition

This paper proposes an efficient maximum margin distance learning (MMDL) method, called DL-PEGASOS, which outperforms state-of-the-art recognition methods on the UCLA benchmark DT dataset and shows that, for certain classes of DTs, spatial texture features are dominantly "salient", while for other classes, this "saliency" lies in their temporal features.
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