A Graph Cut Algorithm for Generalized Image Deconvolution

The goal of deconvolution is to recover an image x from its convolution with a known blurring function. This is equivalent to inverting the linear system y = Hx. In this paper we consider the generalized problem where the system matrix H is an arbitrary non-negative matrix. Linear inverse problems can be solved by adding a regularization term to impose spatial smoothness. To avoid oversmoothing, the regularization term must preserve discontinuities; this results in a particularly challenging energy minimization problem. Where H is diagonal, as occurs in image denoising, the energy function can be solved by techniques such as graph cuts, which have proven to be very effective for problems in early vision. When H is non-diagonal, however, the data cost for a pixel to have a intensity depends on the hypothesized intensities of nearby pixels, so existing graph cut methods cannot be applied. This paper shows how to use graph cuts to obtain a discontinuitypreserving solution to a linear inv...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Ashish Raj, Ramin Zabih
• Keywords: Arbitrary Non-negative Matrix, Computer Vision, Energy Minimization Problem, Graph Cut Methods, Graph Cuts, ICCV 2005, Linear Inverse Problems