Practical cross-sensor color constancy using a dual-mapping strategy
This provides a practical solution for the industry by reducing the need for sensor-specific data recollection, though it is incremental as it builds on existing cross-sensor methods.
The paper tackles the problem of illumination estimation for color constancy across different sensors by proposing a dual-mapping strategy that requires only a simple white point from a test sensor, achieving performance on par with leading cross-sensor methods with minimal memory usage (~0.003 MB) and fast processing times (~0.3 ms on GPU).
Deep Neural Networks (DNNs) have been widely used for illumination estimation, which is time-consuming and requires sensor-specific data collection. Our proposed method uses a dual-mapping strategy and only requires a simple white point from a test sensor under a D65 condition. This allows us to derive a mapping matrix, enabling the reconstructions of image data and illuminants. In the second mapping phase, we transform the re-constructed image data into sparse features, which are then optimized with a lightweight multi-layer perceptron (MLP) model using the re-constructed illuminants as ground truths. This approach effectively reduces sensor discrepancies and delivers performance on par with leading cross-sensor methods. It only requires a small amount of memory (~0.003 MB), and takes ~1 hour training on an RTX3070Ti GPU. More importantly, the method can be implemented very fast, with ~0.3 ms and ~1 ms on a GPU or CPU respectively, and is not sensitive to the input image resolution. Therefore, it offers a practical solution to the great challenges of data recollection that is faced by the industry.