37,347 research outputs found
Color Image Enhancement Method Based on Weighted Image Guided Filtering
A novel color image enhancement method is proposed based on Retinex to
enhance color images under non-uniform illumination or poor visibility
conditions. Different from the conventional Retinex algorithms, the Weighted
Guided Image Filter is used as a surround function instead of the Gaussian
filter to estimate the background illumination, which can overcome the
drawbacks of local blur and halo artifact that may appear by Gaussian filter.
To avoid color distortion, the image is converted to the HSI color model, and
only the intensity channel is enhanced. Then a linear color restoration
algorithm is adopted to convert the enhanced intensity image back to the RGB
color model, which ensures the hue is constant and undistorted. Experimental
results show that the proposed method is effective to enhance both color and
gray images with low exposure and non-uniform illumination, resulting in better
visual quality than traditional method. At the same time, the objective
evaluation indicators are also superior to the conventional methods. In
addition, the efficiency of the proposed method is also improved thanks to the
linear color restoration algorithm.Comment: 15 page
Brain Image Fusion Approach based on Side Window Filtering
Brain medical image fusion plays an important role in framing a contemporary image to enhance the reciprocal and repetitive information for diagnosis purposes. A novel approach using kernel-based image filtering on brain images is presented. Firstly, the Bilateral filter is used to generate a high-frequency component of a source image. Secondly, an intensity component is estimated for the first image. Thirdly, side window filtering is employed on several filters, including the guided filter, gradient guided filter, and weighted guided filter. Thereby minimizing the difference between the intensity component of the first image and the low pass filter of the second image. Finally, the fusion result is evaluated based on three evaluation indexes, including standard deviation (STD), features mutual information (FMI), average gradient (AG). The fused image based on this algorithm contains more information, more details, and clearer edges for better diagnosis. Thus, our fused image-based method is good at finding the position and state of the target volume, which leads to keeping away from the healthy parts and ensuring patients’ soundness
Accelerated graph-based spectral polynomial filters
Graph-based spectral denoising is a low-pass filtering using the
eigendecomposition of the graph Laplacian matrix of a noisy signal. Polynomial
filtering avoids costly computation of the eigendecomposition by projections
onto suitable Krylov subspaces. Polynomial filters can be based, e.g., on the
bilateral and guided filters. We propose constructing accelerated polynomial
filters by running flexible Krylov subspace based linear and eigenvalue solvers
such as the Block Locally Optimal Preconditioned Conjugate Gradient (LOBPCG)
method.Comment: 6 pages, 6 figures. Accepted to the 2015 IEEE International Workshop
on Machine Learning for Signal Processin
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