Image deblurring is a fundamental problem in image processing. Conventional methods often deal with the degraded image as a whole while ignoring that an image contains two different components: cartoon and texture. Recently, total variation (TV) based image decomposition methods are introduced into image deblurring problem. However, these methods often suffer from the well-known stair-casing effects of TV. In this paper, a new cartoon -texture based sparsity regularization method is proposed for non-blind image deblurring. Based on image decomposition, it respectively regularizes the cartoon with a combined term including framelet-domain-based sparse prior and a quadratic regularization and the texture with the sparsity of discrete cosine transform domain. Then an adaptive alternative split Bregman iteration is proposed to solve the new multi-term sparsity regularization model. Experimental results demonstrate that our method can recover both cartoon and texture of images simultaneously, and therefore can improve the visual effect, the PSNR and the SSIM of the deblurred image efficiently than TV and the undecomposed methods.
Real images usually have two layers, namely, cartoons(the piece-wise smooth part of image) and textures(the oscillating
pattern part of the image). In this paper, we solve the challenging image deconvolution problems by using variation
image decomposition method which can regularize the cartoon with total variation and texture in G space respectively.
Different from existing schemes in the literature which can only recover the smooth structure of the image, our
deconvolution method can not only restore the smooth part of image but also recover the detailed oscillating part of the
image. Numerical simulation examples are given to demonstrate the applicability and usefulness of our proposed
algorithms in image deconvolution.
In order to study light scattering from randomly rough surface, the linear filtering method is used to generate Gaussian randomly rough surface, and the method of moments is used to calculate the scattering light intensity distribution from perfect conduct and dielectric surfaces. The calculation results show that scattering characteristics between conductor and dielectric surfaces exist several significant differences: (1) the scattering peak value of perfectly conduct is larger than scattering peak value of dielectric on the same roughness; (2) the difference between s- and ppolarized scattering results are rather small in perfectly conduct randomly rough surfaces, while there is a obvious difference between s- and p-polarized scattering results in the condition of dielectric randomly rough surfaces; (3) though in both conditions of perfectly conduct and dielectric randomly rough surfaces, there is a shift from specular to backscattering direction when incident is p-polarized light, however, in dielectric randomly rough surface situation, the shift is much more obvious than in conduct situation.