Cross-Field Joint Image Restoration via Scale Map

Qiong Yan§      Xiaoyong Shen§      Li Xu§      Shaojie Zhuo
Xiaopeng Zhang    Liang Shen       Jiaya Jia§

§The Chinese Univeristy of Hong Kong       Qualcomm Incorporated


An overview of our cross-field joint image restoration framework. Given image pairs in (a) and (b), our method can get the high-quality restoration result in (c). The s maps in different iterations are shown in (e)-(h).


Color, infrared and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images from different fields, for example, one noisy color image and one dark-flashed near-infrared image. The major issue in such a framework is to handle all structure divergence and find commonly usable edges and smooth transitions for visually plausible image reconstruction. We introduce a novel scale map as a competent representation to explicitly model derivative-level confidence and propose new functions and a numerical solver to effectively infer it following our important structural observations. Our method is general and shows the principled way to solve cross-field restoration problems.



Download ALL high quality images

RGB/flashed-NIR restoration

Noisy Input NIR Image
Click buttons to show results made by different algorithms

RGB/flashed-NIR restoration

Non-flash Image [1] Flash Image [1]
Click buttons to show results made by different algorithms

Day/night image restoration

Day Image [2] Night Image [2]
Click buttons to show different results

Haze Removal

Haze Image [3] NIR Image [3]
Click buttons to show different results

RGB-Depth image restoration

RGB Image Raw Depth Image with Noise
Click buttons to show different results

More results can be found in our supplementary file here.


Snapshot for paper "Cross-Field Joint Image Restoration via Scale Map"
Qiong Yan, Xiaoyong Shen, Li Xu, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Jiaya Jia
IEEE International Conference on Computer Vision(ICCV), 2013

  [Paper (pdf, 2.90MB)] [BibTex]

  [Supplementary file (pdf, 13.5MB)]

 [Matlab executable (zip, 2.5MB)]

  [Results (zip, 35.2MB)]


[1] G. Petschnigg, R. Szeliski, M. Agrawala, M. F. Cohen, H. Hoppe, and K. Toyama. Digital photography with flash and no-flash image pairs. ACM Trans. Graph., 23(3):664-672,2004.

[2] R. Raskar, A. Ilie, and J. Yu. Image fusion for context enhancement and video surrealism. In NPAR, pages 85–152, 2004.

[3] L. Schaul, C. Fredembach, and S. Susstrunk. Color image dehazing using the near-infrared. In ICIP, pages 1629–1632, 2009.



Last update: Oct. 4, 2015