Edge enhancement

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

Image processing method

Abstract: PROBLEM TO BE SOLVED: To enable smoothing selectively changing edge enhancement or strength while considering all band components contained in edges by performing band division processing and performing band synthesis processing after reloading band components while referring to the band components. SOLUTION: In a band dividing step 101, an image to be the object of processing is inputted and such images are divided into plural bands. In a band component change step 102, while referring to one or plural band components, one or plural band components are reloaded. In an image synthesizing step 103, the divided and changed band components are synthesized, made into image and outputted. In this case, the band component of a visually important edge is widely distributed from low frequency component to high frequency component but the band component of visually non-important edge or noise is eccentric in the high frequency component of its strength. Then, in the band dividing step 101, band division is performed while using wavelet transformation.

A morphological transformation for sharpening edges of features before segmentation

Abstract: A gradient-determined grey level morphological opening procedure for edge enhancement is presented. First, the maximum gradient, in the local neighborhood, forms the contribution to the erosion of the centre pixel of that neighborhood. The gradients of the transformed image are then used as contributions to the subsequent dilation of the eroded image. The enhancement procedure is illustrated on sample images representing multiple phases of soil pore structure. Histogram partitioning is applied to the sample images before and after edge enhancement. The resulting segmentation is visually better on the images after enhancement. Stereological measurements made on the segmented images show that the quantity of each phase is preserved but the degree of contact between phases is altered.

Super-resolution through non-linear enhancement filters

Abstract: We proposed a super-resolution system that combines a total variation (TV) filter, shock filter, and learning-based method. In this paper, we ask “what super-resolution should consist of for the texture component of an image?” and propose a new solution that utilizes a TV filter for image decomposition, an improved shock filter for structure component enhancement, and a new non-linear pulse-sharpening filter for texture component enhancement. We obtain good results in terms of picture quality and computational time. We consider this system to be a practical solution, especially for the super-resolution of video signal for devices such as HDTV receivers and PCs with 4K display panels.

Evaluating the effect of infrared image enhancement on human target detection performance and image quality judgment

Abstract: We address the problem of the way in which different, wellknown and commonly used image enhancement techniques effect qualitative human image judgments, and quantitative target detection performance. Two different psychophysical experiments are specially designed and performed to show how smoothing, full dynamic range exploitation, contrast and edge enhancement affect human abilities regarding target detection in IR imagery, and how this effect varies with imaging time and target thermal condition. Analysis of results shows systematic dependence on enhancement methods, very good agreement between image quality judgments and both target detection rates and probabilities. Significant interaction between enhancement methods and the time of day when the image is taken is also found.