2013 International Conference on Communication and Signal Processing 2013
DOI: 10.1109/iccsp.2013.6577178
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FPGA implementation of bilinear interpolation algorithm for CFA demosaicing

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Cited by 13 publications
(6 citation statements)
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“…Several CFA imaging pipelines exist. We can classify them in two large groups: one concerns the hardware and real-time processing community [ 33 , 34 , 35 ], the other concerns the imaging community [ 36 , 37 , 38 ]. A very general distinction is that the former one often considers the problem from the sensor and signal point of view and demosaics the raw image in very early steps, rather the latter considers the problem from a visualization point of view, and demosaics after or jointly with other processing such as white balance.…”
Section: Imaging Pipelinesmentioning
confidence: 99%
“…Several CFA imaging pipelines exist. We can classify them in two large groups: one concerns the hardware and real-time processing community [ 33 , 34 , 35 ], the other concerns the imaging community [ 36 , 37 , 38 ]. A very general distinction is that the former one often considers the problem from the sensor and signal point of view and demosaics the raw image in very early steps, rather the latter considers the problem from a visualization point of view, and demosaics after or jointly with other processing such as white balance.…”
Section: Imaging Pipelinesmentioning
confidence: 99%
“…The color reconstruction module calculates the missing color values by computing the average of the neighboring pixels. 24 There are four general cases indicated by color filters. Because of the Bayer pattern, there are two different color filters for the green shown in Fig.…”
Section: Color Reconstructionmentioning
confidence: 99%
“…In this position, the red filter component of the pixel is already available, but it is necessary to estimate the green and blue components. The missing components can be estimated by applying the following equation: 24,25 Fig. 15 Block diagram of the precolor gain adjustment algorithm.…”
Section: Color Reconstructionmentioning
confidence: 99%
“…Therefore, many researchers have been using interpolation methods to analyze data of different applications. Some of the interpolation methods are as following: B-spline interpolation using linear approximations for setting up viewing and projection matrices and describing complex objects were studied in reference [1], implementing a cubic spline interpolation algorithm on DSP was studied in reference [2], an iterative linear interpolation based on fuzzy gradient model for low-cost VLSI implementation was introduced by reference [3], a polynomial interpolation for space-efficient verifiable secret sharing was given in reference [4], an interpolation method to investigate the improvement in image quality for ground penetrating radar (GPR) acquisition was highlighted in reference [5], gauss interpolation algorithms for nonlinear rational parameters were presented in reference [6], four image interpolation methods for 2-D AR modeling were given in reference [7], a polynomial approach to evaluate the fragmented function approach for two secure two-party computation (STPC) was introduced in reference [8], a bivariate splines in piecewise constant tension as the solution for a functional minimization problem was given in reference [9], a cubic spline interpolation algorithm for smoothness interpolation model of non-circular curve mechanical mold was studied in reference [10], a spline interpolation functions for solution of a non-linear equation was given in reference [11], a view interpolation method from defocused stereo images using linear filtering was introduced in reference [12], a linear interpolation effects on signal transferring was highlighted in reference [13], a novel and fast cubic B-splines algorithm for cancellation of random valued impulse noise was given in reference [14], a real time implementation of cubic B-spline algorithm for electro optical tracking system was studied in reference [15], a cubic B-spline curves based research of the approximate algorithm was given in reference [16], and a fast algorithm for quadratic and cubic spline wavelets was provided in reference [17]. Among these, the spline curve makes it easy to build an interface that will allow designing and controlling the shape of complex curves and surfaces by using low-degree polynomials in each of the intervals and by choosing the polynomial pieces such that they fit smoothly together.…”
Section: Introductionmentioning
confidence: 99%