Noise-suppressed image enhancement using multiscale top-hat selection transform through region extraction.

Enhancing an image through increasing the contrast of the image is one effective way of image enhancement. To well enhance an image and suppress the produced noises in the resulting image, a multiscale top-hat selection transform-based algorithm through extracting bright and dark image regions and increasing the contrast between them is proposed. First, the multiscale top-hat selection transform is discussed and then is used to extract the bright and dark image regions of each scale. Second, the final extracted bright and dark image regions are obtained through a maximum operation on all the extracted multiscale bright and dark image regions at all scales. Finally, by using a weight strategy, the image is enhanced through increasing the contrast of the image by adding the final bright regions on and subtracting the final dark regions from the original image. The weight parameters are used to adjust the effect of image enhancement. Because the multiscale top-hat selection transform is used to effectively extract the final image regions and discriminate the possible noise regions, the image is well enhanced and some noises are suppressed. Experimental results on different types of images show that our algorithm performs well for noise-suppressed image enhancement and is useful for different applications.