Deconvolution Sample Clauses
Deconvolution. Each party shall pay fifty percent (50%) of the ------------- aggregate costs incurred in connection with the conduct of Deconvolution on Lead Extracts and the designation of Lead Molecules, including without limitation fully loaded costs of all employees and/or consultants and all out-of-pocket expenses (collectively, "Deconvolution Costs"). After the commencement of Deconvolution activities, each party shall on a calendar quarter basis prepare a report to the Committee detailing all Deconvolution Costs incurred by it during such quarter and aggregate Deconvolution Costs less reimbursements hereunder. To the extent that any such report shows aggregate Deconvolution Costs incurred by one party in an amount exceeding those incurred by the other by at least [ ]*, the party incurring the lesser Deconvolution Costs shall, within [ ]* after receipt of such report, reimburse the party incurring the greater costs to the extent that such other party's Deconvolution Costs exceed 50% of the aggregate Deconvolution Costs incurred by both parties. Estimates of costs associated with their respective tasks, as such tasks are set forth in detail on Appendix A, ---------- shall be furnished by both Phytera and Galileo as soon as practicable following a determination by the Committee to undertake Deconvolution with respect to a Lead Extract. Such estimates shall be approved in advance by the Committee, and precise terms of cost reimbursement shall be stated in writing by the Committee prior to commencement of Deconvolution.
Deconvolution. In applications such as astronomy, medicine, physics and biology, scientists use digital images to record and analyze results from experiments. Environmental effects and imperfections in the imaging system can cause the recorded images to be degraded by blurring and noise. Image restoration (sometimes known as deblurring or deconvolution) is the process of reconstructing or estimating the true image from the degraded one. Image deblurring algorithms can be classified into two types: spectral filtering methods and iterative methods. Another classification divides these algorithms into methods that do not require any information about the blur (also called blind deconvolution algorithms) and methods that need that information. In this work we only discuss the latter ones. Information about the blur is usually given in the form of a point spread function (PSF). A PSF is an image that describes the response of an imaging system to a point object. A theoretical PSF can be obtained based on the optical properties of the imaging system. The main advantage of this approach is that the obtained PSF is noise- free. The experimental technique, on the other hand, relies on taking a picture of a point object, for example in astronomy this can be a distant star. Mathematically, image deblurring is the process of computing an approximation of a vector f true (which represents the true image scene) from the linear inverse problem (1.5). Here, K is a large, usually ill-conditioned matrix defined by the PSF, and g is a vector representing the recorded image, which is degraded by blurring and noise. We assume that the PSF, and hence K, is known, but the noise η is unknown. Because K is usually severely ill-conditioned, some form of regularization needs to be incorporated. As was already mentioned in Section 1.3, many regularization methods compute solutions of the form f reg = K†rg, (2.1) where K†r can be thought of as a regularized pseudo-inverse of K. The precise form of K†r depends on many things, including the regularization method, the data g, and the blurring matrix K [57]. Note that f reg = Kr† g = Kr† Kf true + K†rη , (2.2) so such regularization methods attempt to balance the desire to have K†rK ≈ I while at the same time keeping K†rη from becoming too large.