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Mohsin Bilal,Muhammad Shams-ur-Rehman,Muhammad Arfan Jaffar 한국산학기술학회 2013 SmartCR Vol.3 No.4
Pseudo-deconvolution is an effective approach to restore space variant degradation (SVD), where each sub-problem is treated as an optimization problem that simultaneously performs image de-blurring and de-noising. De-blurring is an inverse problem primary to visual processing systems. It becomes ill-posed if noise taints the blurry image. Thus the problem is very sensitive to small perturbation in the data. Additive noise has made obsolete classical approaches of inverse filtering and linear algebraic restorations. The restoration formulation for the ill-posed inverse degradations is constrained least square error (CLSE) minimization. Generally, regularization of solutions by “smoothness constraint” is an addition in the classical approaches to cater to the sensitivity of solutions for small perturbations. In this paper, two well-known evolutionary computation (EC) algorithms: 1) the genetic algorithm (GA) with binary and real encoding schemes, and 2) the particle swarm algorithm (PSO), are proposed to evolve the estimated image in order to obtain an optimal solution for restoration with adaptive regularization. Thus the restoration framework presented in this paper is new and novel, such that it reconstructs the image guided by evolutionary computations. Furthermore, modifications in the initial evolutionary framework are proposed to make it a novel hybrid meta-heuristic approach for real-world restoration applications. Quantitative and visual results of the proposed framework are presented in the paper, with comparative analysis within the EC domain and state-of-the-art methods.
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Unorthodox approach toward microscopic shape from image focus using optical microscopy
Mutahira, Husna,Muhammad, Mannan Saeed,Jaffar, Arfan,Choi, Tae‐,Sun Wiley Subscription Services, Inc., A Wiley Company 2013 Microscopy research and technique Vol.76 No.1
<P><B>Abstract</B></P><P>Shallow depth‐of‐field is an inherent property of optical microscope. Because of this limitation, it is usually impossible to image large three‐dimensional (3D) objects entirely in focus. However, the in‐focus information of the object's surface can be acquired over a range of images by optical sectioning of the object in consideration. These images can then be processed to generate a single in‐focus image and further for 3D shape reconstruction using methods like Shape from focus (SFF). SFF represents a passive technique for recovering object shapes. Although numerous methods for SFF have been recently proposed, all follow similar precedent of focus measure application and depth recovery by maximizing the focus curves. As the conventional techniques assume the presence of prominent texture in the scene, the shape of weak textured surfaces are not recovered properly. In this manuscript, we have followed an unorthodox approach to recover shapes of microscopic objects using SFF. At first, the in‐focus image is obtained, pursued by computing depth along the edges and their neighbors present in scene. Empty spaces in the final depth map are then calculated by surface interpolation. The proposed approach works well even for objects with weak textures. Microsc. Res. Tech., 2013. © 2012 Wiley Periodicals, Inc.</P>
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