Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Preserving the sharpness of edge structures is highly challenging to image interpolation. ,  propose an edge-oriented two-step interpolation method that utilizes an edge training set. For edge interpolation, the LR edge map is converted into the HR edge map by using the training set. Then, an image is classified into smooth and edge regions using the HR edge map, and both regions are interpolated separately. For edge regions, adaptive edge-oriented interpolation is performed by using the detailed edge structures learned from training. The proposed method is extensively evaluated, and its performance is compared with the conventional edge-based methods. Experimental results show that the proposed method can not only reconstruct the missed edge information by the training set, but also significantly reduce blurring and jagging artifacts around edges by separately interpolating smooth and edge regions.
['Ji Hoon Lee', 'Jong Ok Kim', 'Jong Woo Han', 'Kang Sun Choi', 'Sung Jea Ko']
I am writing a research paper on a idea Edge-oriented two-step interpolation based on training set
[{"from": "human", "value": "I am writing a research paper on a idea Edge-oriented two-step interpolation based on training set"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Preserving the sharpness of edge structures is highly challenging to image interpolation. ,  propose an edge-oriented two-step interpolation method that utilizes an edge training set. For edge interpolation, the LR edge map is converted into the HR edge map by using the training set. Then, an image is classified into smooth and edge regions using the HR edge map, and both regions are interpolated separately. For edge regions, adaptive edge-oriented interpolation is performed by using the detailed edge structures learned from training. The proposed method is extensively evaluated, and its performance is compared with the conventional edge-based methods. Experimental results show that the proposed method can not only reconstruct the missed edge information by the training set, but also significantly reduce blurring and jagging artifacts around edges by separately interpolating smooth and edge regions."}]