The present invention relates to the processing of digital images, and specifically, to a system for estimating and/or reducing the amount of image blur in digital images.
Digital representation of an image presents a wide array of advantages over analog representation of images, e.g. negative film, positive slide, etc. Digital images, for example, may be electronically stored, transmitted, and copied without degradation of the image. Digital images of objects and events may be captured by traditional devices such as a digital camera, scanned from a negative or picture by a scanner or may be computer-generated. Digital images may also be digitally processed and manipulated to change image characteristics such as color and contrast, or reduce defects or artifacts in the image, such as red-eye from a camera flash or image blur.
Image blur is one of the most important factors in the perception of image quality due to the attendant loss of image detail and sharpness. Image blur may be the result of a number of things, including for example, camera lenses not focused on the subject, aperture blur of the foreground and/or the background, relative motion of the subject with respect to the camera, and even atmospheric haze. Image blur in traditional, analog images such as color photographs historically could not be perceptibly reduced. Image blur in digital images, however, can be perceptibly reduced. Existing processing techniques for reducing image blur typically include both a blur estimation component and a blur reduction component. If the blur estimation component detects image blur beyond a predetermined threshold, the image is processed by the blur reduction component. Some existing processing techniques for reducing image blur quantify the image blur and adjust the correction accordingly. Other techniques for reducing image blur employ an iterative process that repeatedly cycles the image through the blur reduction component until the estimated amount of image blur is below the predetermined threshold.
One existing system of estimating image blur is done in the spatial domain. In this system, sharp edges are used to generate a point-spread function and the amount of image blur is estimated from this function. A frequent problem encountered with this system is that many real-world images lack sufficiently sharp edges to reliably estimate image blur.
Another system of estimating image blur estimates the amount image blur from the power spectrum of the image in the frequency domain. This system takes advantage of the assumption that the power spectrum of most real-world, non-blurred images is relatively invariant where the power at non-zero frequencies is generally inversely proportional to the square of frequency. Blurred images, conversely, exhibit a characteristic where the power falls more rapidly as frequency increases. This phenomenon is illustrated in FIG. 1, which shows the power spectrum of both a blurred image and a non-blurred image. As can be seen from FIG. 1, the power of the blurred image is approximately equal to that of the non-blurred image at very low frequencies, but quickly drops below that of the non-blurred image at frequencies greater than approximately 0.05 cycles/sample. Because they uses scene statistics, systems that estimate image blur from the power spectrum of the image are generally considered to be more reliable than systems that estimate image blur from a point spread function derived from image structure, such as sharp edges. Systems that estimate image blur from the power spectrum of the image, however, are computationally complex, making implementation difficult in many applications, such as a photo printer.
Yet another system for estimating image blur is disclosed by Zhang et al., U.S. Pat. No. 6,298,145 B1. This system estimates the amount of image blur in a digital image from a weighted occurrence histogram of all non-zero DCT coefficients contained in the digital image file that stores the digital image. As stated in this reference, however, this system “does not use the [DCT] coefficients directly since their values are closely related to the type of image they depict.” Instead, the DCT components are used to construct the aforementioned occurrence histogram, which must then be mathematically manipulated, adding computational complexity.
What is desired, then, is a computationally efficient system for estimating and/or reducing image blur.
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