Removing undesired reflections from a single-image is inherently ill-posed problem since defining its exact physical model is almost impossible. Most previous works tackle this problem through the use of multiple images or hand-crafted features. These methods are still quite limited in terms of the generality and photo-realistic result. In this paper, we propose a conditional Generative Adversarial Networks based deep neural network structure to render realistic image and formulate our problem in a simple objective function. Specifically, we use gradient information to elaborate this formulation to preserve both low and high frequency details. Our proposed network does not rely on any physical prior information and performs effectively with a single-image. Experimental results demonstrate that proposed algorithm conducts favorably against existing algorithms from human perceptual aspect.
|Title of host publication||ICEIC 2019 - International Conference on Electronics, Information, and Communication|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 May 3|
|Event||18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand|
Duration: 2019 Jan 22 → 2019 Jan 25
|Name||ICEIC 2019 - International Conference on Electronics, Information, and Communication|
|Conference||18th International Conference on Electronics, Information, and Communication, ICEIC 2019|
|Period||19/1/22 → 19/1/25|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the Technology Innovation Program funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (No. 10073229).
© 2019 Institute of Electronics and Information Engineers (IEIE).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering