Digital images are commonly used in a wide variety of applications. For example, digital television, computers, cellular telephones, digital cameras, and gaming consoles, all display and/or manipulate digital image content in various image formats.
A digital image is a two-dimensional representation of an image scene using set of spatially located picture elements or pixels. Digital images must often represent a large range of intensity values occurring in the image scene using a limited number of quantized intensity levels (e.g. 256 quantized intensity values for an 8-bit image). In certain circumstances, image capture and display technology limitations may result in poorly displayed contrast between pixels having similar intensities. The resulting image, in turn, appears to have limited detail in areas of similar pixel intensity.
Contrast enhancement may be applied to images in an attempt to correct such losses in image detail by altering intensity values of certain pixels in the image. Contrast enhancement generally involves determining which of the pixel intensity values to alter, and then applying a tone mapping function to the input image pixels to produce output image pixels having improved image detail, for example.
Automating the determination of which of the pixels to alter, is difficult in practice. Manual contrast enhancement may be applied to still images by manipulating the tone mapping function and observing the result until a visually appealing result is achieved. Such manual manipulations are time consuming and are also not useful for automated contrast enhancement, such as may be required for video streams that include a plurality of sequential image frames.
Histogram equalization has been used to provide automatic contrast enhancement for images. Histogram equalization attempts to spread out most frequently occurring intensity values in an image over the available intensity range. However, commonly used methods of histogram equalization may result in unrealistic representation of image scenes.
Accordingly, there remains a need for efficient methods of contrast enhancement for digital images.
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