High Contrast Images

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Summary: A mathematical model has been developed at UCLA that performs inpainting of high contrast images with considerable decrease in processing time. The results produced by this model are comparable to existing methods, making this technique ideal for document and image processing. Background: Inpainting has been practiced by art curators for many years to repair damaged paintings, where the visible patterns are used to make assumptions on how to fill in the missing pieces. In recent years, the advent of digitization gave rise to various mathematical models that would automate the task of interpreting patterns on a digital image for filling in the empty spaces. Common applications of inpainting include sharpening of blurry images, as well as the reduction of noise (i.e. scratches and speckles) in an image. Existing mathematical models involve complex computations requiring extensive time to approximate the complete image, which creates interest for a faster method that does not sacrifice image quality. Innovation:   Researchers studying image processing at the UCLA has devised a robust technique for high-contrast images that overcomes the time-consuming aspect of existing inpainting models. The simplicity of the UCLA model allows it to compute the missing pieces efficiently with significantly less processing time. Furthermore, the simplified model is capable of generating an image comparable to that produced by traditional image processing algorithms. The technique has been compared to the existing models on an assortment of images, including printed text and aerial photographs. Quantitative data demonstrates marked improvement in calculation time, as depicted in the following table. Testing was conducted on two examples-inpainting a circle, and inpainting a disconnected stripe. The speed made possible by this innovation will allow rapid computation of large datasets.  - See more at: https://techtransfer.universityofcalifornia.edu/NCD/20280.html#sthash.A3Yh5coH.dpuf