Method for enhancing compressibility and visual quality of scanned document images

Abstract

A system and method of image processing for smoothing, denoising, despeckling and sharpening scanned document images which is performed prior to a compression. The scanned image is selectively smoothed by anisotropic diffusion filtering in a single iteration with a 3×3 kernel, which provides denoising, edge-preserving smoothing. The smoothed image data is then selectively sharpened using variable contrast mapping that provides overshoot-free variable-sharpening and despeckling. Image quality is improved while increasing compressibility of the image.

Description

FIELD OF THE INVENTION The present invention relates to processing of image data and in particular to enhancing quality and compressibility of digit images including combinations of text, graphics, and natural images by selective smoothing / denoising and selective sharpening. BACKGROUND OF THE INVENTION Digital image data is often processed to enhance the visual quality of the image. Common image processing techniques include image smoothing and image sharpening. Smoothing is a technique that is mainly performed for reducing certain types of noise. Non-selective (or linear) smoothing algorithms smooth all features in an image (i.e., areas in the image which can be characterized as flat regions and areas within the image which can be characterized as edges). However, it is undesirable to smooth edges since smoothed edges gives the image a “blurry” appearance. Moreover, although smoothing is effective in removing most Gaussian noise, it is less effective in removing high amplitude no...

AI Technical Summary

Benefits of technology

A system and method of enhancing image data and increasing compressibility of data by selectively smoothing the image data while preserving edges and selectively sharpening image data using variable contrast stretching. In one embodiment, variable contrast stret

Problems solved by technology

However, it is undesirable to smooth edges since smoothed edges gives the image a “blurry” appearance.

Moreover, although smoothing is effective in removing most Gaussian noise, it is less effective in removing high amplitude noise such as speckle noise.

One disadvantage of non-selective sharpening techniques is that they also tend to amplify noise.

Although this technique shows that anisotropic diffusion can be used to improve image compressibility, it is impractical for real-time image processing applications such as image scanning since many time consuming iterations are required to obtain the desired image quality and compressibility.

In addition, the conventional anisotropic diffusion technique does not clean speckle noise and other types of high amplitude noise and is thus insufficient for pre-processing scanned document images.

However, this technique is still not fast enough for applications for processing full-page images or real-time image processing.

However, application of these techniques on document images-containing text, graphics, and natural images was not considered since it is well known that denoising filters (particularly applied in many iterations) degrade the quality of textual and graphical images.

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