1 research outputs found
Edge Adaptive Hybrid Regularization Model For Image Deblurring
The parameter selection is crucial to regularization based image restoration
methods. Generally speaking, a spatially fixed parameter for regularization
item in the whole image does not perform well for both edge and smooth areas. A
larger parameter of regularization item reduces noise better in smooth areas
but blurs edge regions, while a small parameter sharpens edge but causes
residual noise. In this paper, an automated spatially adaptive regularization
model, which combines the harmonic and TV models, is proposed for
reconstruction of noisy and blurred images. In the proposed model, it detects
the edges and then spatially adjusts the parameters of Tikhonov and TV
regularization terms for each pixel according to the edge information.
Accordingly, the edge information matrix will be also dynamically updated
during the iterations. Computationally, the newly-established model is convex,
which can be solved by the semi-proximal alternating direction method of
multipliers (sPADMM) with a linear-rate convergence rate. Numerical simulation
results demonstrate that the proposed model effectively reserves the image
edges and eliminates the noise and blur at the same time. In comparison to
state-of-the-art algorithms, it outperforms other methods in terms of PSNR,
SSIM and visual quality