Restoration of Color Components in an Image Model

Abstract

This invention relates to a method for improving image quality of a digital image captured with an imaging module comprising at least imaging optics and an image sensor, where the image is formed through the imaging optics, the image consisting of at least one colour component. In the method, the degradation information of each colour component of the image is found and is used for improving image quality. The degradation information of each colour component is specified by a point-spread function. Each colour component is restored by the degradation function. The image can be unprocessed image data. The invention also relates to several alternatives for implementing the restoration, and for controlling and regularizing the inverse process independently of the image degradation. The invention also relates to a device, to a module, to a system and to a computer program products and to a program modules.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application is for entry into the U.S. national phase under §371 for International Application No. PCT / FI05 / 050001 having an international filing date of Jan. 4, 2005, and from which priority is claimed under all applicable sections of Title 35 of the United States Code including, but not limited to, Sections 120, 363 and 365(c). This application is also a continuation-in-part of U.S. patent application Ser. No. 10 / 888,534, filed on Jul. 9, 2004, from which domestic priority is claimed.FIELD OF THE INVENTION[0002]This invention relates to image processing and particularly to a restoration of colour components in a system for storage or acquisition of digital images.BACKGROUND OF THE INVENTION[0003]Blurring or degradation of an image can be caused by various factors, e.g. out-of-focus optics, or any other aberrations that result from the use of a wide-angle lens, or the combination of inadequate aperture value, focal length and lens p...

AI Technical Summary

Benefits of technology

"The present invention provides a method and system for improving the quality of images captured with imaging modules such as cameras. The method involves finding degradation information of each color component in the image, obtaining a degradation function based on the degradation information, and restoring each color component by the degradation function. The invention can reduce blurring caused by optics, resulting in sharper and more accurate images. The invention can also be used as a pre-processing operation to improve the quality of image reconstruction and reduce non-linearities accumulated in the image capturing process. The invention provides a more viable and effective solution for improving image quality in future products."

Problems solved by technology

Blurring or degradation of an image can be caused by various factors, e.g. out-of-focus optics, or any other aberrations that result from the use of a wide-angle lens, or the combination of inadequate aperture value, focal length and lens positioning.

During the image capture process, when long exposure times are used, the movement of the camera, or the imaged subject, can result in motion blurring of the picture.

Also, when short exposure time is used, the number of photons being captured is reduced, this results in high noise levels, as well as poor contrast in the captured image.

However, most of these techniques do not consider the image reconstruction process in the modelling of the problem, and assume simplistic linear models.

Typically, the solutions in implementations are quite complicated and computationally demanding.

Usually, after a few iterations, there is not much improvement between adjacent steps.

The continuation of the de-blurring algorithm beyond a certain point might introduce annoying artefacts into the restored image, such as e.g. overshooting of the edges due to over-emphasis of the details or even false colouring.

Their use in consumer products is limited, due to the difficulty of quantifying the image gathering process and the typical complexity and computational power needed to implement these algorithms.

The exact modelling of the image acquisition process is more difficult and (in most cases) is not linear.

If the restoration is applied after the compression (which is typically lossy), the result can amplify unwanted blocking artefacts.

Images