Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution.

By Dongliang Cheng, Dilip K. Prasad, Michael S. Brown. Journal of The Optical Society of America A-optics Image Science and Vision, 2014

359 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/58acc16bb633fd0cef2d53e0ed35f6eb4956ff89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Yang Cao
2. Tali Treibitz
3. Derya Akkaynak

In Cognitive Neuroscience

1. Yang Cao
2. Tali Treibitz
3. Derya Akkaynak

In Social Psychology

1. Tali Treibitz
2. Derya Akkaynak

Citations in the corpus

• A deep CNN method for underwater image enhancement (2017, methodology): “Thus, many previous work use local smoothness assumption to address color correction [13] [14] [15] [16] [17] [18] and haze removal [19] [20] [21] [22] [23] [24] problems, which assumes that the color distortion and”
• Sea-Thru: A Method for Removing Water From Underwater Images (2019, methodology): “This can be done using statistical or learning based methods (see [32] for a survey); here for the scenes that contain a sufficiently diverse set of colors, we adopt the simple and fast Gray World Hypothesis [14], and for monochromatic scenes (such as in our dataset D4 we introduce next), we use a s”