Efficient fourier-wavelet super-resolution.

Ricoh Innovations, Menlo Park, CA 94025 USA.
IEEE Transactions on Image Processing (Impact Factor: 3.11). 05/2010; 19(10):2669-81. DOI: 10.1109/TIP.2010.2050107
Source: PubMed

ABSTRACT Super-resolution (SR) is the process of combining multiple aliased low-quality images to produce a high-resolution high-quality image. Aside from registration and fusion of low-resolution images, a key process in SR is the restoration and denoising of the fused images. We present a novel extension of the combined Fourier-wavelet deconvolution and denoising algorithm ForWarD to the multiframe SR application. Our method first uses a fast Fourier-base multiframe image restoration to produce a sharp, yet noisy estimate of the high-resolution image. Our method then applies a space-variant nonlinear wavelet thresholding that addresses the nonstationarity inherent in resolution-enhanced fused images. We describe a computationally efficient method for implementing this space-variant processing that leverages the efficiency of the fast Fourier transform (FFT) to minimize complexity. Finally, we demonstrate the effectiveness of this algorithm for regular imagery as well as in digital mammography.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Key objective of super-resolution (SR) is to overcome the ill-posed conditions of image acquisition. SR facilitates better content visualization and scene recognition from low resolution images. In this paper we present a new robust super resolution approach. Our approach, firstly registers two input image using SIFT-BP-RANSAC registration. Secondly due to the importance of information gain ratio of SR, the fuzzy inference system will fuse the registered image with the reference image to aggregate the amount of details from both input images. According to the diversity of acquisition model classes, an adaptive fuzzy approach has been developed to robustly fuse the integrated information of input images into a high resolution image. Our approach iteratively minimizes the difference between the resulted high resolution image and the ground truth image. Independence from the acquisition model leads to the robustness of our method on different ill-posed capturing conditions. Our final results indicate better achievements in comparison with similar recent works in the literature.
    Electrical Engineering (ICEE), 2014 22nd Iranian Conference on; 05/2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.
    Machine Vision and Applications 08/2014; 25(6):1423-1468. DOI:10.1007/s00138-014-0623-4 · 1.44 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution which efficiently addresses the presence of outliers in the low-resolution images is proposed. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the iterative algorithm, and a parameter weighing the data fidelity term with respect to the smoothness term. Apart from the robust estimation of the high-resolution image, the contribution of the proposed method is twofold: (1) the robust computation of the regularization parameters controlling the relative strength of the prior with respect to the data fidelity term and (2) the robust estimation of the optimal step size in the update of the high-resolution image. Experimental results demonstrate that integrating these estimations into a robust framework leads to significant improvement in the accuracy of the high-resolution image.
    Journal of Electronic Imaging 07/2014; 23(4):043016. DOI:10.1117/1.JEI.23.4.043016 · 0.85 Impact Factor

Full-text (2 Sources)

Available from
Jun 4, 2014