RESUMEN
The novel field of fluid lens cameras introduces unique image processing challenges. Intended for surgical applications, these fluid optics systems have a number of advantages over traditional glass lens systems. These advantages include improved miniaturization and no moving parts while zooming. However, the liquid medium creates two forms of image degradation: image distortion, which warps the image such that straight lines appear curved, and nonuniform color blur, which degrades the image such that certain color planes appear sharper than others. We propose the use of image processing techniques to reduce these degradations. To deal with image warping, we employ a conventional method that models the warping process as a degree-six polynomial in order to invert the effect. For image blur, we propose an adapted perfect reconstruction filter bank that uses high frequency sub-bands of sharp color planes to improve blurred color planes. The algorithm adjusts the number of levels in the decomposition and alters a prefilter based on crude knowledge of the blurring channel characteristics. While this paper primarily considers the use of a sharp green color plane to improve a blurred blue color plane, these methods can be applied to improve the red color plane as well, or more generally adapted to any system with high edge correlation between two images.
RESUMEN
Due to the novel fluid optics, unique image processing challenges are presented by the fluidic lens camera system. Developed for surgical applications, unique properties, such as no moving parts while zooming and better miniaturization than traditional glass optics, are advantages of the fluid lens. Despite these abilities, sharp color planes and blurred color planes are created by the nonuniform reaction of the liquid lens to different color wavelengths. Severe axial color aberrations are caused by this reaction. In order to deblur color images without estimating a point spread function, a contourlet filter bank system is proposed. Information from sharp color planes is used by this multiband deblurring method to improve blurred color planes. Compared to traditional Lucy-Richardson and Wiener deconvolution algorithms, significantly improved sharpness and reduced ghosting artifacts are produced by a previous wavelet-based method. Directional filtering is used by the proposed contourlet-based system to adjust to the contours of the image. An image is produced by the proposed method which has a similar level of sharpness to the previous wavelet-based method and has fewer ghosting artifacts. Conditions for when this algorithm will reduce the mean squared error are analyzed. While improving the blue color plane by using information from the green color plane is the primary focus of this paper, these methods could be adjusted to improve the red color plane. Many multiband systems such as global mapping, infrared imaging, and computer assisted surgery are natural extensions of this work. This information sharing algorithm is beneficial to any image set with high edge correlation. Improved results in the areas of deblurring, noise reduction, and resolution enhancement can be produced by the proposed algorithm.