We present a deep network to recover pixel values lost to clipping. The clipped area of the image is typically a uniform area of minimum or maximum brightness, losing image detail and color fidelity. The degree to which the clipping is visually noticeable depends on the amount by which values were clipped, and the extent of the clipped area. Clipping may occur in any (or all) of the pixel's color channels. Although clipped pixels are common and occur to some degree in almost every image we tested, current automatic solutions have only partial success in repairing clipped pixels and work only in limited cases such as only with overexposure (not under-exposure) and when some of the color channels are not clipped. Using neural networks and their ability to model natural images allows our neural network, DeclipNet, to reconstruct data in clipped regions producing state of the art results.
|Original language||American English|
|Title of host publication||2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - 29 Aug 2018|
|Event||25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece|
Duration: 7 Oct 2018 → 10 Oct 2018
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||25th IEEE International Conference on Image Processing, ICIP 2018|
|Period||7/10/18 → 10/10/18|
Bibliographical noteFunding Information:
This research was supported by the Israel Science Foundation and by the Israel Ministry of Science and Technology.
© 2018 IEEE.
- Image enhancement
- Image restoration
- Neural networks