Edge enhancement

About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.

A systems approach to custom VLSI for a digital color imaging system

Abstract: An analysis of system requirements is presented that guides the design of a chip set that provides all the required functionality and control for single-sensor color imaging systems. The first device is a color filter array (CFA) processor that processes the single stream of sparse color information from an unconventional CFA pattern and produces a full-resolution color image in real time. The second chip is a red, green, and glue (RGB) processor that improves the image quality of the reconstructed RGB data by performing black-level adjustment, color matrixing, gamma correction, and edge enhancement, again in real time. The third device is a timing controller with an architecture specifically suited to imaging systems. The chips are algorithm specific, and the algorithms, architectures, and design methodology are detailed. The chip set is readily applicable to slide and negative film-to-video converters, electronic still cameras, and component or composite video cameras. It is capable of operation with NTSC, CCIR 601, and PAL/SECAM video standards. >

Fuzzy morphology for edge detection and segmentation

Abstract: This paper proposes a new approach for structure based separation of image objects using fuzzy morphology. With set operators in fuzzy context, we apply an adaptive alpha-cut morphological processing for edge detection, image enhancement and segmentation. A Top-hat transform is first applied to the input image and the resulting image is thresholded to a binary form. The image is then thinned using hit-or-miss transform. Finally, m-connectivity is used to keep the desired number of connected pixels. The output image is overlayed on the original for enhanced boundaries. Experiments were performed using real images of aerial views, sign boards and biological objects. A comparison to other edge enhancement techniques like unsharp masking, sobel and laplacian filtering shows improved performance by the proposed technique.

Edge compensated transform coding

Abstract: Transform based image compression has difficulty with image regions containing edges. Edge compensated transform coding (ECTC) addresses this problem by preprocessing to remove edges. This preprocessing is adapted to transform coding. The edge information is sent in a side channel and the edges are replaced at the receiver. Subjective improvement is demonstrated. >

Edge enhancement with background noise reduction in video image processing

Abstract: The present invention is directed to a method and apparatus for image edge enhancement with background noise reduction. According to the method and apparatus, background noise is reduced through use of feed forward gain control and threshold control of a sharpness control amplifier. In a prior art circuit, the sharpness control amplifier was controlled only by a sharpness control signal. By controlling the sharpness control amplifier also with a feed forward gain control and a threshold control, the circuit can be made to have background noise reduction while maintaining a continuous input/output characteristic curve. According to the input/output characteristic curve, when the amplitude of the transitions of the video signal are below a particular threshold value, the amplification of the sharpness control amplifier is reduced by the gain control, such that low amplitude noise signals are reduced. When the amplitude of the transitions of the video signal, representing image edge transitions, are above the amplitude of the threshold level, then normal signal amplification is produced. In this manner, the edges of the image are enhanced while background noise is reduced.

Image enhancement by edge-preserving filtering

Abstract: Image enhancement is useful when the details in an image are lost due to various reasons. It is common to subtract a mask from a given image to enhance the details. The trick is how to obtain a good mask. We describe here how an edge-preserving filter can be used to generate a mask which is smooth over areas with fine details, yet preserving most of the edges. Experiments with real images show that our scheme is very effective. >