Improving Image Restoration with Soft-Rounding

Several important classes of images such as text, barcode and pattern images have the property that pixels can only take a distinct subset of values. This knowledge can benefit the restoration of such images, but it has not been widely considered in current restoration methods. In this work, we describe an effective and efficient approach to incorporate the knowledge of distinct pixel values of the pristine images into the general regularized least squares restoration framework. We introduce a new regularizer that attains zero at the designated pixel values and becomes a quadratic penalty function in the intervals between them. When incorporated into the regularized least squares restoration framework, this regularizer leads to a simple and efficient step that resembles and extends the rounding operation, which we term as soft-rounding. We apply the soft-rounding enhanced solution to the restoration of binary text/barcode images and pattern images with multiple distinct pixel values. Experimental results show that soft-rounding enhanced restoration methods achieve significant improvement in both visual quality and quantitative measures (PSNR and SSIM). Furthermore, we show that this regularizer can also benefit the restoration of general natural images.Comment: 9 pages, 6 figure

A partnership-based, whole-watershed approach to climate adaptation in Acadia National Park

Changes in climate and associated changes in seasonality, invasive plants and insects, and visitation are stressing ecosystems and infrastructure in Acadia National Park. Over the past five years, park staff and partners have begun taking an interdisciplinary, partnership-based approach to assessing baseline conditions, identifying stresses, developing climate change scenarios, and restoring the ecological and cultural integrity and resilience of whole watersheds. The approach contrasts with past resource management in which managers frequently tackled problems with minimal coordination between disciplines (e.g., water, wildlife, cultural resources, and maintenance) and locations. The result has been a series of projects that have begun to measurably improve the health of one of the park’s most visited and iconic watersheds: the Cromwell Brook watershed, which includes Sieur de Monts (Acadia began in 1916 as Sieur de Monts National Monument) and the Great Meadow, and whose namesake waterway flows through the gateway town of Bar Harbor. Projects (inside and out of the park) have included rehabilitating a historic spring pool, replacing undersized culverts with open-bottom bridges, removing a poorly sited septic system, removing invasive plants, restoring native wetland, establishing monitoring to assess changes in watershed health, and working with the town and other stakeholders to plan future projects that would further improve the health of Great Meadow and downstream areas in Bar Harbor. The combination of planning; monitoring; restoring healthy, functioning ecological communities; and minimizing stresses from human infrastructure and visitation offer the best chance of main- taining Acadia National Park for the enjoyment of future generations

Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems

Optimization methods are at the core of many problems in signal/image processing, computer vision, and machine learning. For a long time, it has been recognized that looking at the dual of an optimization problem may drastically simplify its solution. Deriving efficient strategies which jointly brings into play the primal and the dual problems is however a more recent idea which has generated many important new contributions in the last years. These novel developments are grounded on recent advances in convex analysis, discrete optimization, parallel processing, and non-smooth optimization with emphasis on sparsity issues. In this paper, we aim at presenting the principles of primal-dual approaches, while giving an overview of numerical methods which have been proposed in different contexts. We show the benefits which can be drawn from primal-dual algorithms both for solving large-scale convex optimization problems and discrete ones, and we provide various application examples to illustrate their usefulness