Evaluation of and compensation for spatial noise of LCDs in medical applications.

Recent developments in liquid crystal display (LCD) technology suggest that this technology will replace the cathode ray tube (CRT) as the most popular softcopy display technology in the medical arena. However, LCDs are far from ideal for medical imaging. One of the principal problems they possess is spatial noise contamination, which requires accurate characterization and appropriate compensation before LCD images can be effectively utilized for reliable diagnosis. This paper presents some work we have conducted recently on characterization of spatial noise of high resolution LCDs. The primary purpose of this work is to explore the properties of spatial noise and propose a method to reduce it. A high quality CCD camera was used for physical evaluation. Spatial noise properties were analyzed and estimated from the camera images via signal modeling and processing. A noise compensation algorithm based on error diffusion was developed to process images before they were displayed. Results shown in this paper suggest that LCD spatial noise can be effectively reduced via appropriate processing.

Mathematical extrapolation of image spectrum for constraint-set design and set-theoretic superresolution.

Several powerful iterative algorithms are being developed for the restoration and superresolution of diffraction-limited imagery data by use of diverse mathematical techniques. Notwithstanding the mathematical sophistication of the approaches used in their development and the potential for resolution enhancement possible with their implementation, the spectrum extrapolation that is central to superresolution comes in these algorithms only as a by-product and needs to be checked only after the completion of the processing steps to ensure that an expansion of the image bandwidth has indeed occurred. To overcome this limitation, a new approach of mathematically extrapolating the image spectrum and employing it to design constraint sets for implementing set-theoretic estimation procedures is described. Performance evaluation of a specific projection-onto-convex-sets algorithm by using this approach for the restoration and superresolution of degraded images is outlined. The primary goal of the method presented is to expand the power spectrum of the input image beyond the range of the sensor that captured the image.

Image preprocessing for improving computational efficiency in implementation of restoration and superresolution algorithms.

Computational complexity is a major impediment to the real-time implementation of image restoration and superresolution algorithms in many applications. Although powerful restoration algorithms have been developed within the past few years utilizing sophisticated mathematical machinery (based on statistical optimization and convex set theory), these algorithms are typically iterative in nature and require a sufficient number of iterations to be executed to achieve the desired resolution improvement that may be needed to meaningfully perform postprocessing image exploitation tasks in practice. Additionally, recent technological breakthroughs have facilitated novel sensor designs (focal plane arrays, for instance) that make it possible to capture megapixel imagery data at video frame rates. A major challenge in the processing of these large-format images is to complete the execution of the image processing steps within the frame capture times and to keep up with the output rate of the sensor so that all data captured by the sensor can be efficiently utilized. Consequently, development of novel methods that facilitate real-time implementation of image restoration and superresolution algorithms is of significant practical interest and is the primary focus of this study. The key to designing computationally efficient processing schemes lies in strategically introducing appropriate preprocessing steps together with the superresolution iterations to tailor optimized overall processing sequences for imagery data of specific formats. For substantiating this assertion, three distinct methods for tailoring a preprocessing filter and integrating it with the superresolution processing steps are outlined. These methods consist of a region-of-interest extraction scheme, a background-detail separation procedure, and a scene-derived information extraction step for implementing a set-theoretic restoration of the image that is less demanding in computation compared with the superresolution iterations. A quantitative evaluation of the performance of these algorithms for restoring and superresolving various imagery data captured by diffraction-limited sensing operations are also presented.