Abstract
Guided image filter (GIF) is an edge-preserving filtering technique that smooths the fine texture of an input image with the guide of a second image. One shortcoming of GIF and all its existing variants, such as, weighed guided image filter and gradient domain guided image filter, is that they only use one grayscale image as the guide and are consequently unable to fully utilize the rich information offered by multichannel images. In this paper, we extend GIF for multichannel guidance image and propose a novel correlation detection technique for retaining sharp edges with opposite gradient directions in the different channels of the same guidance image. Experimental results show that the proposed method preserves the details from both the input and guidance images better than existing GIF techniques.
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