Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In edge-preserving image smoothing, edge blurriness and structural edge attenuation have been common problems.   $L_0$   smoothing successfully solves these two problems by adopting   $L_0$   norm of gradients. Hover, a ak structural edge diminishing problem still exists because    $L_0$   penalty first removes small nonzero gradients. In order to address this problem,  propose superpixel-guided adaptive image smoothing by introducing an adaptive parameter into    $L_0$   smoothing framework. The adaptive smoothing parameter is efficiently computed in a cascade manner. In the first stage,  allocate smoothing parameters to the pixels consisting of details. More importantly,  then exploit similarities beten a pixel and its surrounding superpixels for assigning smoothing parameters to the rest pixels. Experimental results demonstrate that our proposed method efficiently preserves structural edges regardless of their scales compared to previous methods.
['Hyunjun Eun', 'Changick Kim']
I am writing a research paper on a idea Superpixel-Guided Adaptive Image Smoothing
[{"from": "human", "value": "I am writing a research paper on a idea Superpixel-Guided Adaptive Image Smoothing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In edge-preserving image smoothing, edge blurriness and structural edge attenuation have been common problems.   $L_0$   smoothing successfully solves these two problems by adopting   $L_0$   norm of gradients. Hover, a ak structural edge diminishing problem still exists because    $L_0$   penalty first removes small nonzero gradients. In order to address this problem,  propose superpixel-guided adaptive image smoothing by introducing an adaptive parameter into    $L_0$   smoothing framework. The adaptive smoothing parameter is efficiently computed in a cascade manner. In the first stage,  allocate smoothing parameters to the pixels consisting of details. More importantly,  then exploit similarities beten a pixel and its surrounding superpixels for assigning smoothing parameters to the rest pixels. Experimental results demonstrate that our proposed method efficiently preserves structural edges regardless of their scales compared to previous methods."}]