KEYWORDS: Image restoration, Image processing, Wavelets, Chemical elements, Deconvolution, Compressed sensing, Image resolution, Point spread functions, Radio over Fiber, Detection and tracking algorithms
In this paper, proposed an image restoration method which base on the sparse constraint. Based on the principle of Compressed Sensing, the observed image is transformed into the wavelet domain, and then converted the image restoration problem to a convex set unrestricted optimization problem by limiting the number of non-zero elements of the wavelet domain, using the gradient projection method for solving the optimization problem to achieve the restoration of the input image. Experiments show that the method presented has the fast convergence and good robustness compared to the traditional total variation regularization restoration method.
In this paper, we propose an algorithm for Moving Object Detecting which can remove influence of shadow and
illumination change. The algorithm is based on background subtraction using color and texture information, we establish
a texture model based on LBP (local binary pattern) for each pixel, and adopt a newly developed photometric invariant
color measurement to description color information, Use a similarly pixel-based models update algorithm that proposed
by Stauffer et al, but the difference is that we use a novel ‘hysteresis’ scheme for update of the weight. We use two layer
process in foreground detecting, at the pixel layer, through the texture and color model we mentioned above to divide the
each pixel to background or foreground, at the another layer, calculate the LBP texture information for the foreground
regions boundaries which come out by color model subtraction, through comparing them to texture information come out
by texture model for the foreground regions boundaries to remove fault detect of foreground.
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