Image smoothing based on histogram equalized content-aware patches and direction-constrained sparse gradients

Abstract

For better removing textures with larger gradients while preserving structural edges with smaller gradients, we propose an image smoothing method based on histogram equalized content-aware patches and direction-constrained sparse gradients. In order to better smooth the boundary concentration regions while maintaining the continuity of boundary pixels, a content-aware patching technology with boundary constraints is proposed. The irregular patches are represented by the smallest rectangular bounding boxes to reduce the computational complexity. Based on edge information, patches are divided into edge-patches and non-edge-patches. Histogram equalization is used to improve the edge gradient of patches with structural edge concentration, while the image decomposition is used to reduce the gradient of texture details. Taking the edge information as the smoothing factor, each patch is smoothed via direction-constrained sparse gradients. The entire image needs to be further smoothed to remove residual texture details. Experimental results show that new method has better visual effects in retaining structural edges and removing texture details, and has many applications, including edge detection, image abstraction, detail enhancement, and texture mapping.