Efficient Fourier-Wavelet Super-Resolution

Ricoh Innovations, Menlo Park, CA 94025 USA.
IEEE Transactions on Image Processing (Impact Factor: 3.63). 05/2010; 19(10):2669-81. DOI: 10.1109/TIP.2010.2050107
Source: DBLP

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.

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Available from: Cynthia Toth, Sep 27, 2015
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    • "An alternative computationally efficient yet generally suboptimal approach is to separate the denoising and interpolation processes. This can be achieved by first interpolating, which may be accompanied by deblurring and registration, and then denoising one [18] or multiple frames [19]. An alternative approach reverses the order by denoising the data first followed by interpolation [20]. "
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    ABSTRACT: In this paper we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography (SDOCT) images captured in the clinic.qualitatively and quantitatively outperforms other state-of-theart methods.
    07/2013; 32(11). DOI:10.1109/TMI.2013.2271904
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    • "Improved image acquisition protocols may be used to optimize data collection for widefield mosaicing. It may be possible to mosaic image with less overlap than was used in this study, if the more accurate (yet more computationally expensive) multi-frame joint image registration algorithms are utilized [46–48]. By increasing the distance between retinal locations imaged, total imaging time per subject may be reduced or a wider field may be imaged. "
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    ABSTRACT: Variance processing methods in Fourier domain optical coherence tomography (FD-OCT) have enabled depth-resolved visualization of the capillary beds in the retina due to the development of imaging systems capable of acquiring A-scan data in the 100 kHz regime. However, acquisition of volumetric variance data sets still requires several seconds of acquisition time, even with high speed systems. Movement of the subject during this time span is sufficient to corrupt visualization of the vasculature. We demonstrate a method to eliminate motion artifacts in speckle variance FD-OCT images of the retinal vasculature by creating a composite image from multiple volumes of data acquired sequentially. Slight changes in the orientation of the subject's eye relative to the optical system between acquired volumes may result in non-rigid warping of the image. Thus, we use a B-spline based free form deformation method to automatically register variance images from multiple volumes to obtain a motion-free composite image of the retinal vessels. We extend this technique to automatically mosaic individual vascular images into a widefield image of the retinal vasculature.
    Biomedical Optics Express 06/2013; 4(6):803-21. DOI:10.1364/BOE.4.000803 · 3.65 Impact Factor
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    • "θ 0 are the parameters of affine motions. We use the variable projection to estimate θ 0 [5], [26]. We minimize: "
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    ABSTRACT: Modern digital cameras are quickly reaching the fundamental physical limit of their native resolution. Super-resolution (SR) aims at overcoming this limit. SR combines several images of the same scene into a high resolution image by using differences in sampling caused by camera motion. The main difficulty encountered when designing SR algorithms is that the general SR problem is ill-posed. Assumptions on the regularity of the image are then needed to perform SR. Thanks to advances in regularization priors for natural images, producing visually plausible images becomes possible. However, regularization may cause a loss of details. Therefore, we argue that regularization should be used as sparingly as possible, especially when the restored image is needed for further precise processing. This paper provides principles guiding the local choice of regularization parameters for SR. With this aim, we give an invertibility condition for affine SR interpolation. When this condition holds, we study the conditioning of the interpolation and affine motion estimation problems. We show that these problems are more likely to be well posed for a large number of images. When conditioning is bad, we propose a local total variation regularization for interpolation and show its application to multi-image demosaicking.
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