Deconvolution is a computationally intensive image processing technique that is being increasingly utilized for improving the contrast and resolution of digital images captured in the microscope. The foundations are based upon a suite of methods that are designed to remove or reverse the blurring present in microscope images induced by the limited aperture of the objective. The most commonly utilized algorithms for deconvolution in optical microscopy can be divided into two classes: deblurring and image restoration. Deblurring algorithms are fundamentally two-dimensional, because they apply an operation plane-by-plane to each two-dimensional plane of a three-dimensional image stack. In contrast, image restoration algorithms are properly termed "three-dimensional" because they operate simultaneously on every pixel in a three-dimensional image stack.
The inventor has developed a method for accurately deconvolving multiple point-spread functions from a single image. The developed function is convex, can locate 3D particles and lines in images, reconstruct images, and deconvolute blurred images. This invention has developed a function that is convex, which means it can be quickly and accurately minimized to find a solution. It also allows for the use of convex optimization routines that are fast, robust, and accurate.
Reference Number: D-1199
3D locating of particles and lines in images
3D reconstructions of images
Advanced deconvolution of blurred images
Features, Benefits & Advantages:
Accurate deconvolution of blurred images
The invention has been produced and tested.
A U.S. Provisional Application was filed 04/21/16.
Craig Snoeyink, Ph.D., Assistant Professor, Mechanical Engineering, Texas Tech University, Lubbock, Texas.
Keywords: deconvolution of image, multiple point spread functions