Rank-order and morphological enhancement of image details with an optoelectronic processor

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.

Superresolved imaging of remote moving targets

We present a superresolving approach that allows one to exceed the diffraction limit and recover highly resolved contours of moving targets from a sequence of low-resolution images. The presented approach is suitable for remote sensing applications. The resolution decoding algorithm that is used to recover the high-resolution features of the target can be run partially via optical means and that way can be used to reduce the required computational complexity.

Optical implementation of visible gray-image morphology with the visual-area-coding technique

We present a novel scheme of visible gray-image morphology with the visual-area-coding technique (VACT). The VACT is a technique of digitized analog-optical computing in which data are converted into visible coded patterns and processed with the visible form. Because the achievable operations in the VACT are identical to those of mathematical morphology, mathematical morphology is adapted to gray-image morphology with the VACT. Computer simulation and optical experiments of the several operations in mathematical morphology verify the correctness of the proposed technique. The processing capacity of the proposed method is estimated in terms of the space-bandwidth product.