Gradient Domain Weighted Guided Image Filtering
This addresses halo artifacts in image processing for applications like denoising and enhancement, but it is incremental as it builds on the well-known guided image filter.
The paper tackled halo artifacts in guided image filtering by using gradient information and weighted distinctions between edges and flat areas, resulting in sharper edges and reduced blur. Experimental results showed significant suppression of halo artifacts, making it effective for image denoising and detail enhancement.
Guided image filter is a well-known local filter in image processing. However, the presence of halo artifacts is a common issue associated with this type of filter. This paper proposes an algorithm that utilizes gradient information to accurately identify the edges of an image. Furthermore, the algorithm uses weighted information to distinguish flat areas from edge areas, resulting in sharper edges and reduced blur in flat areas. This approach mitigates the excessive blurring near edges that often leads to halo artifacts. Experimental results demonstrate that the proposed algorithm significantly suppresses halo artifacts at the edges, making it highly effective for both image denoising and detail enhancement.