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Pharmaceutical drug development costs have risen rapidly over the past twenty years. Yet, the number of new molecular entities being approved has not increased. Most of these costs can be traced to work on compounds that are abandoned before getting to market. As pharmaceutical companies scale back their work in light of deteriorating R&D productivity, significant unmet medical needs remain unaddressed. More extensive external licensing of the IP associated with abandoned compounds could spur recovery and/or redeployment of abandoned compounds to address at least some of these unmet medical needs. The example of Cubicin is offered as an illustration of these more open approaches. This article inductively develops a simple framework to guide companies in the decision of whether and when to license out unused internal IP. [ABSTRACT FROM AUTHOR]

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters. [ABSTRACT FROM AUTHOR]