Multiscale image sharpening adaptive to edge profile

The color image has different edge profiles depending on the objects placed in the scene. We propose a novel image-sharpening method adaptive to the local edge slopes with the suppression of background noises. Prescanning the image by a Gaussian derivative (GD) filter, we generate the edge map, which classifies the edge areas to hard, medium, and soft edges, and separates the flat areas without edges. Multiple GD filters with different standard deviations are selectively applied to sharpen each segmented edge area by looking up the edge map. To keep the gray balance, the edge-sharpening filters are applied only to the luminance image. In flat areas except edges, the sharpening filters are resumed and instead, a Gaussian smoothing filter is applied to reduce the background noises. The proposed method brings a dramatic improvement in the reduction of flat-area noises and the natural image-sharpening effects adaptive to the edge slopes. In addition, we newly introduce quality assessment indices to evaluate the image sharpness and flat-area noises with the experimental data.

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