International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Digital Image Processing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Patch Based Blind Image Super Resolution
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compressive Structured Light for Recovering Inhomogeneous Participating Media
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
A novel framework for imaging using compressed sensing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Image up-sampling using total-variation regularization with a new observation model
IEEE Transactions on Image Processing
PiCam: an ultra-thin high performance monolithic camera array
ACM Transactions on Graphics (TOG)
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This paper proposes a new algorithm to generate a super-resolution image from a single, low-resolution input without the use of a training data set. We do this by exploiting the fact that the image is highly compressible in the wavelet domain and leverage recent results of compressed sensing (CS) theory to make an accurate estimate of the original high-resolution image. Unfortunately, traditional CS approaches do not allow direct use of a wavelet compression basis because of the coherency between the point-samples from the downsampling process and the wavelet basis. To overcome this problem, we incorporate the downsampling low-pass filter into our measurement matrix, which decreases coherency between the bases. To invert the downsampling process, we use the appropriate inverse filter and solve for the high-resolution image using a greedy, matchingpursuit algorithm. The result is a simple and efficient algorithm that can generate high quality, high-resolution images without the use of training data. We present results that show the improved performance of our method over existing super-resolution approaches.