As a high-resolution imaging mode of biological tissues and materials, optical coherence tomography (OCT) is widely used in medical diagnosis and analysis. However, OCT images are often degraded by annoying speckle noise inherent in its imaging process. Employing the bilateral sparse representation an adaptive singular value shrinking method is proposed for its highly sparse approximation of image data. Adopting the generalized likelihood ratio as similarity criterion for block matching and an adaptive feature-oriented backward projection strategy, the proposed algorithm can restore better underlying layered structures and details of the OCT image with effective speckle attenuation. The experimental results demonstrate that the proposed algorithm achieves a state-of-the-art despeckling performance in terms of both quantitative measurement and visual interpretation.
Image denoising while preserving image features is a key problem in image processing and computer vision. This letter proposes an adaptive mixed method for image restoration. First, this method decomposes a given image as the sum of two components: geometric structure and oscillating pattern according to Meyer's theory. Second, a coupled bidirectional diffusion equation is used to restore the structure part, and a nonlocal means filter is used to remove noise in the oscillating part. Experimental results show advantages of this method in feature-preserving denoising.
In the past decade there has been a growing amount of research concerning partial differential equations in image sharpening. Most of these models indicate edges by a binary zero-crossing decision, however, which will produce a false result with piecewise constant regions, whose textures and fine part are lost. In this paper, we propose a feature preserving coupled bidirectional flow process, where an inverse diffusion is performed to sharpen edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove noise and artifacts ("jaggies") along the tangent directions on the contrary. To preserve image features, the nonlinear diffusion coefficients are locally adjusted according to the directional derivatives of the image. Experimental results demonstrate that our algorithm substantially improves the subjective quality of the enhanced images.