# DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks

## TL;DR

DeepIlluminance introduces a deep neural network with contextual and refinement modules for more accurate scene illumination estimation, addressing local ambiguity issues and achieving competitive results on benchmark datasets.

## Contribution

The paper presents a novel contextual deep network with a stage-wise training strategy for improved illuminant estimation in color constancy tasks.

## Key findings

- Achieves competitive performance on illuminant estimation benchmarks.
- Utilizes a center-surround architecture for local contextual feature extraction.
- Employs a refinement network to enhance initial estimates.

## Abstract

Computational color constancy refers to the estimation of the scene illumination and makes the perceived color relatively stable under varying illumination. In the past few years, deep Convolutional Neural Networks (CNNs) have delivered superior performance in illuminant estimation. Several representative methods formulate it as a multi-label prediction problem by learning the local appearance of image patches using CNNs. However, these approaches inevitably make incorrect estimations for the ambiguous patches affected by their neighborhood contexts. Inaccurate local estimates are likely to bring in degraded performance when combining into a global prediction. To address the above issues, we propose a contextual deep network for patch-based illuminant estimation equipped with refinement. First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches. The patches are sampled based on the observation that pixels with large color differences describe the illumination well. Then, the refinement net integrates the input patches with the corrected patches in conjunction with the use of intermediate features to improve the performance. To train such a network with numerous parameters, we propose a stage-wise training strategy, in which the features and the predicted illuminant from previous stages are provided to the next learning stage with more finer estimates recovered. Experiments show that our approach obtains competitive performance on two illuminant estimation benchmarks.