Upsampling

About: Upsampling is a research topic. Over the lifetime, 2426 publications have been published within this topic receiving 57613 citations.

DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution With Large Factors

Abstract: Recently, single-image super-resolution has made great progress due to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a predefined upsampling operator, such as bicubic interpolation, to upscale input low-resolution images to the desired size and learn nonlinear mapping between the interpolated image and ground truth high-resolution (HR) image. However, interpolation processing can lead to visual artifacts as details are over smoothed, particularly when the super-resolution factor is high. In this paper, we propose a deep recurrent fusion network (DRFN), which utilizes transposed convolution instead of bicubic interpolation for upsampling and integrates different-level features extracted from recurrent residual blocks to reconstruct the final HR images. We adopt a deep recurrence learning strategy and, thus, have a larger receptive field, which is conducive to reconstructing an image more accurately. Furthermore, we show that the multilevel fusion structure is suitable for dealing with image super-resolution problems. Extensive benchmark evaluations demonstrate that the proposed DRFN performs better than most current deep learning methods in terms of accuracy and visual effects, especially for large-scale images, while using fewer parameters.

Multi-carrier receiver for wireless communication

Abstract: A multi-carrier receiver capable of receiving one or multiple frequency channels simultaneously is described. In one design, the multi-carrier receiver includes a single radio frequency (RF) receive chain, an analog-to-digital converter (ADC), and at least one processor. The RF receive chain processes a received RF signal and provides an analog baseband signal comprising multiple signals on multiple frequency channels. The ADC digitizes the analog baseband signal. The processor(s) digitally processes the samples from the ADC to obtain an input sample stream. This digital processing may include digital filtering, DC offset cancellation, I/Q mismatch compensation, coarse scaling, etc. The processor(s) digitally downconverts the input sample stream for each frequency channel to obtain a downconverted sample stream for that frequency channel. The processor(s) then digitally processes each downconverted sample stream to obtain a corresponding output sample stream. This digital processing may include digital filtering, downsampling, equalization filtering, upsampling, sample rate conversion, fine scaling, etc.

Nonlinear Image Upsampling Method Based on Radial Basis Function Interpolation

Abstract: In this paper, we present a novel edge-directed upsampling method based on radial basis function (RBF) interpolation. In order to remove artifacts such as blurred edges or blocking effects, we suggest a nonlinear method capable of taking edge information into account. The resampling evaluation is determined according to the edge orientation. The proposed scheme is as simple to implement as linear methods but it demonstrates improved visual quality by preserving the edge features better than the classical linear interpolation methods. The algorithm is compared with some well-known linear schemes as well as recently developed nonlinear schemes. The resulting images demonstrate the new algorithm's ability to magnify an image while preserving the edge features.

Graph Signal Processing – A Probabilistic Framework

Abstract: This theoretical paper aims to provide a probabilistic framework for graph signal processing. By modeling signals on graphs as Gaussian Markov Random Fields, we present numerous important aspects of graph signal processing, including graph construction, graph transform, graph downsampling, graph prediction, and graph-based regularization, from a probabilistic point of view. As examples, we discuss a number of methods for constructing graphs based on statistics from input data sets; we show that the graph transform is the optimal linear transform to decorrelate the signal; we describe the optimality of the Kron reduction for graph downsampling in a probabilistic sense; and we derive the optimal predictive transform coding scheme applicable to both motion prediction and intra predictive coding.

Image restoration : fundamentals and advances

Abstract: Image Denoising: Past, Present, and Future, X. Li Historical Review of Image Denoising First Episode: Local Wiener Filtering Second Episode: Understanding Transient Events Third Generation: Understanding Nonlocal Similarity Conclusions and Perspectives Fundamentals of Image Restoration, B.K. Gunturk Linear Shift-Invariant Degradation Model Image Restoration Methods Blind Image Restoration Other Methods of Image Restoration Super Resolution Image Restoration Regularization Parameter Estimation Beyond Linear Shift-Invariant Imaging Model Restoration in the Presence of Unknown Spatially Varying Blur, M. Sorel and F. Sroubek Blur models Space-Variant Super Resolution Image Denoising and Restoration Based on Nonlocal Means, P. van Beek, Y. Su, and J. Yang Image Denoising Based on the Nonlocal Means Image Deblurring Using Nonlocal Means Regularization Recent Nonlocal and Sparse Modeling Methods Reducing Computational Cost of NLM-Based Methods Sparsity-Regularized Image Restoration: Locality and Convexity Revisited, W. Dong and X. Li Historical Review of Sparse Representations From Local to Nonlocal Sparse Representations From Convex to Nonconvex Optimization Algorithms Reproducible Experimental Results Conclusions and Connections Resolution Enhancement Using Prior Information, H.M. Shieh, C.L. Byrne, and M.A. Fiddy Fourier Transform Estimation and Minimum L2-Norm Solution Minimum Weighted L2-Norm Solution Solution Sparsity and Data Sampling Minimum L1-Norm and Minimum Weighted L1-Norm Solutions Modification with Nonuniform Weights Summary and Conclusions Transform Domain-Based Learning for Super Resolution Restoration, P.P. Gajjar, M.V. Joshi, and K.P. Upla Introduction to Super Resolution Related Work Description of the Proposed Approach Transform Domain-Based Learning of the Initial HR Estimate Experimental Results Conclusions and Future Research Work Super Resolution for Multispectral Image Classification, F. Li, X. Jia, D. Fraser, and A. Lambert Methodology Experimental Results Color Image Restoration Using Vector Filtering Operators, R. Lukac Color Imaging Basics Color Space Conversions Color Image Filtering Color Image Quality Evaluation Document Image Restoration and Analysis as Separation of Mixtures of Patterns: From Linear to Nonlinear Models, A. Tonazzini, I. Gerace, and F. Martinelli Linear Instantaneous Data Model Linear Convolutional Data Model Nonlinear Convolutional Data Model for the Recto-Verso Case Conclusions and Future Prospects Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera, Y.-W. Tai and M.S. Brown Related Work Hybrid Camera System Optimization Framework Deblurring of Moving Objects Temporal Upsampling Results and Comparisons