TY - GEN
T1 - Gradient adaptive image restoration and enhancement
AU - Wang, Hongcheng
AU - Chen, Yunqiang
AU - Fang, Tong
AU - Tyan, Jason
AU - Ahuja, Narendra
PY - 2006
Y1 - 2006
N2 - Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.
AB - Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.
KW - Gradient methods
KW - Image enhancement
KW - Medical image processing
KW - Partial differential equations
UR - http://www.scopus.com/inward/record.url?scp=62349120765&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62349120765&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2006.313034
DO - 10.1109/ICIP.2006.313034
M3 - Conference contribution
AN - SCOPUS:62349120765
SN - 1424404819
SN - 9781424404810
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2893
EP - 2896
BT - 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
T2 - 2006 IEEE International Conference on Image Processing, ICIP 2006
Y2 - 8 October 2006 through 11 October 2006
ER -