Image resolution has important applications in printers, scanners, or simply any devices related to the visual display of images. Image resolution enhancement is commonly used where the original image source has a low resolution, such as a video image source. A second common usage of image resolution enhancement is where there is blurring of images by linear interpolation. Linear interpolation is often used where an image is enlarged from a smaller image.
To improve the perceived image resolution, the high frequency component of the image typically needs to be augmented. A popular method for improving image resolution used in some hardcopy devices to sharpen images is "unsharp masking." Unsharp masking is a linear filtering process that can be mathematically expressed as y=x+.alpha.(h*x) where x is the input signal, y is the output signal, h is the highpass filter, and .alpha. is the sharpening gain. Implementation of the unsharp masking process according to the above-identified expression extracts the high frequency component of the input signal by highpass filtering and then weights the extracted component by multiplying it by the gain, .alpha.. The weighted component .alpha.(h*x) is then added to the input signal to increase the high frequency content.
Although unsharp masking is generally effective in improving the image resolution, it has the disadvantage of causing edge overshoots. A further disadvantage of unsharp masking process is that it does not differentiate between image characteristics such as edges and textures. Thus, it is difficult to attain significant image enhancement using unsharp masking without producing either overly sharpened edges and/or texture.
A computationally efficient, image enhancement system which improves image sharpness without producing either overly sharpened edges and/or texture is needed.