Practical cross-sensor color constancy using a dual-mapping strategy
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Color ConstancyCPUGPUSimilar Papers 제목 키워드 기반
Unsupervised Learning for Color Constancy
Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based…
Color ConstancyGCC: Generative Color Constancy via Diffusing a Color Checker
Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illum…
Color ConstancyData AugmentationWhite-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation
Color constancy aims to keep object colors consistent under varying illumination. Cross-camera generalization in color constancy remains challenging because learning-based models often overfit to the color response chara…
Color ConstancyCross-Camera Convolutional Color Constancy
We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camer…
Color ConstancyCPUGPUDeep Neural Models for color discrimination and color constancy
Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance with varying cues. Inputs to the network…
Color Constancy