A Bayesian super-resolution approach to demosaicing of blurred images
EURASIP Journal on Applied Signal Processing
Video-to-video dynamic super-resolution for grayscale and color sequences
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
Demosaicking based on optimization and projection in different frequency bands
Journal on Image and Video Processing - Color in Image and Video Processing
Robust color image superresolution: an adaptive M-estimation framework
Journal on Image and Video Processing - Color in Image and Video Processing
Joint Blind Super-Resolution and Shadow Removing
IEICE - Transactions on Information and Systems
Registration errors: are they always bad for super-resolution?
IEEE Transactions on Signal Processing
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
SoftCuts: a soft edge smoothness prior for color image super-resolution
IEEE Transactions on Image Processing
Regularization approaches to demosaicking
IEEE Transactions on Image Processing
Self-similarity driven color demosaicking
IEEE Transactions on Image Processing
Real time turbulent video perfecting by image stabilization and super-resolution
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Storage-efficient quasi-Newton algorithms for image super-resolution
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Video enhancement using a robust iterative SRR based on Leclerc stochastic estimation
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Video luminance transient improvement using difference-of-Gaussian
APCC'09 Proceedings of the 15th Asia-Pacific conference on Communications
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Adaptive multiple-frame image super-resolution based on U-curve
IEEE Transactions on Image Processing
Color kernel regression for robust direct upsampling from raw data of general color filter array
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Color image demosaicking: An overview
Image Communication
Journal of Signal Processing Systems
Feature salience for neural networks: comparing algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Accurate image registration for MAP image super-resolution
Image Communication
PiCam: an ultra-thin high performance monolithic camera array
ACM Transactions on Graphics (TOG)
A unified framework for multi-sensor HDR video reconstruction
Image Communication
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In the last two decades, two related categories of problems have been studied independently in image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteriori estimation technique by minimizing a multiterm cost function. The L1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.