Abstract. We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes. Key words. denoising, segmentation DOI. 10.1137/040615286 1. Introduction. Image denoising and segmentation are two related, fundamental problems of computer vision. The goal of denoising is to remove noise and/or spurious details from a given, possibly corrupted, digital picture while maintaining essential features such as edges. The goal of segmentation is to divide the image into regions that belong to distinct objects in the depicted scene. Approaches to denoising and segmentation based on the calculus of variations and partial differential equations (PDEs) have had great success. One important reason for their success is that these models are particularly well suited to imposing geometric constraints (such as regularity) on the solutions sought. Among the best known and most influential examples are the Rudin-Osher-Fatemi (ROF) total variation-based image denoising model [22] and the Mumford-Shah image segmentation model [17] . Denoising models such as the ROF model can be easily adapted to different situations. An interesting scenario is the denoising of shapes: Here, the given image is binary (representing the characteristic function of the given shape), and the noise is in the geometry of the shape: Its boundary might be very rough, and the user might be interested in smoothing out its boundary, and perhaps removing small, unnecessary connected components of the shape. This task is a common first step in many object detection and recognition algorithms. A common difficulty with many variational image processing models is that the energy functional to be minimized has local minima (which are not global minima). This is a much more serious drawback than nonuniqueness of global minimizers (which is also a common phenomenon) because local minima of segmentation and denoising models often have completely wrong levels of detail and scale: whereas global minimizers of a given model are usually all reasonable solutions, the local minima tend to be blatantly false. Many solution techniques for variational models are based on gradient descent, and are therefore prone to getting stuck in such local minima. This
Specifically, they use the model which is proposed in REF which is a covexification of the two-phase version of the Mumford-Shah model.
Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
SIAM Journal on Applied Mathematics
Computer Science Mathematics