Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Analysis and Design of Analog Integrated Circuits
Analysis and Design of Analog Integrated Circuits
Super-Resolution Imaging
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Mosaicing with Super-Resolution Zoom
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Perceptually-Inspired and Edge-Directed Color Image Super-Resolution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning-based nonparametric image super-resolution
EURASIP Journal on Applied Signal Processing
Single Frame Super-Resolution: A New Learning Based Approach and Use of IGMRF Prior
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Neighbor embedding based super-resolution algorithm through edge detection and feature selection
Pattern Recognition Letters
An improved super-resolution with manifold learning and histogram matching
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Superresolution of License Plates in Real Traffic Videos
IEEE Transactions on Intelligent Transportation Systems
Super-Resolution of Face Images Using Kernel PCA-Based Prior
IEEE Transactions on Multimedia
Design of a Class D Audio Amplifier IC Using Sliding Mode Control and Negative Feedback
IEEE Transactions on Consumer Electronics
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An adaptive Gaussian model for satellite image deblurring
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
IEEE Transactions on Image Processing
Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing
Interval-valued fuzzy sets for color image super-resolution
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Eigentransformation-based face super-resolution in the wavelet domain
Pattern Recognition Letters
Image super-resolution: use of self-learning and gabor prior
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Hi-index | 0.01 |
In this paper, we propose a new learning-based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a database consisting of low and high spatial resolution images, we obtain super-resolution for the test image. We first obtain an initial high-resolution (HR) estimate by learning the high-frequency details from the available database. A new discrete wavelet transform (DWT) based approach is proposed for learning that uses a set of low-resolution (LR) images and their corresponding HR versions. Since the super-resolution is an ill-posed problem, we obtain the final solution using a regularization framework. The LR image is modeled as the aliased and noisy version of the corresponding HR image, and the aliasing matrix entries are estimated using the test image and the initial HR estimate. The prior model for the super-resolved image is chosen as an Inhomogeneous Gaussian Markov random field (IGMRF) and the model parameters are estimated using the same initial HR estimate. A maximum a posteriori (MAP) estimation is used to arrive at the cost function which is minimized using a simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting the experiments on gray scale as well as on color images. The method is compared with the standard interpolation technique and also with existing learning-based approaches. The proposed approach can be used in applications such as wildlife sensor networks, remote surveillance where the memory, the transmission bandwidth, and the camera cost are the main constraints.