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Multi-frame demosaicing and super-resolution of color images
- IEEE Trans. on Image Processing
, 2006
"... In the last two decades, two related categories of problems have been studied independently in the 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 ..."
Abstract
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Cited by 53 (8 self)
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In the last two decades, two related categories of problems have been studied independently in the 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 MAP estimation technique by minimizing a multi-term cost function. The L 1 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
Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences
, 2006
"... We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking i ..."
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Cited by 13 (3 self)
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We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed method is based on a very fast and memory efficient approximation of the Kalman filter (KF). Experimental results on both simulated and real data are supplied, demonstrating the presented algorithms, and their strength.
1Multi-Frame Demosaicing and Super-Resolution of Color Images
"... In the last two decades, two related categories of problems have been studied independently in the 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 ..."
Abstract
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In the last two decades, two related categories of problems have been studied independently in the 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 MAP estimation technique by minimizing a multi-term 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
ADMM algorithm for demosaicking deblurring denoising
"... The paper is concerned with the problem of demosaicking, deblurring and denoising a color image in the same time. The global model of the acquisition chain for a color image contains these three effects, then doing restoration in the same time as demosaicking makes sense. We propose to take into acc ..."
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The paper is concerned with the problem of demosaicking, deblurring and denoising a color image in the same time. The global model of the acquisition chain for a color image contains these three effects, then doing restoration in the same time as demosaicking makes sense. We propose to take into account for correlation of spectral bands (R,G,B colors) by minimizing a criterion written in a nearly decorrelated basis. Then we adapt the Alternating Direction Multipliers Minimization (ADMM) method to get the solution. 1
UNIVERSITY OF CALIFORNIA SANTA CRUZ A FAST AND ROBUST FRAMEWORK FOR IMAGE FUSION AND ENHANCEMENT
, 2005
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Super-Resolution using Image Sequence in Raw Sensor Domain
"... Abstract — Although the performance of imaging sensors is constantly improving, there are still several physical and practical constraints that limit the final image quality. In this paper, we present a framework for producing a high-resolution color image directly from a sequence of images captured ..."
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Abstract — Although the performance of imaging sensors is constantly improving, there are still several physical and practical constraints that limit the final image quality. In this paper, we present a framework for producing a high-resolution color image directly from a sequence of images captured by a CMOS sensor that is overlaid with a color filter array. The algorithm attempts to utilize the additional temporal resolution in order to improve the demosaicing of the color data and filter the noisy and blurred image data. The method is based on iterative super-resolution that performs separately the filtering of the individual color image planes. We present experimental results using synthetic image sequence as well with real data from CMOS sensors. I.
