Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network
This work addresses the need for efficient and accurate CRF modeling in computer vision, offering a significant speed improvement for calibration tasks.
The paper tackles the problem of modeling camera response functions (CRF) by proposing a new model using a single latent variable and a fully connected neural network, achieving state-of-the-art accuracy in benchmark tests and being almost twice as fast as current models during calibration.
Modelling the mapping from scene irradiance to image intensity is essential for many computer vision tasks. Such mapping is known as the camera response. Most digital cameras use a nonlinear function to map irradiance, as measured by the sensor to an image intensity used to record the photograph. Modelling of the response is necessary for the nonlinear calibration. In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed. The model is produced using unsupervised learning with an autoencoder on real-world (example) camera responses. Neural architecture searching is then used to find the optimal neural network architecture. A latent distribution learning approach was introduced to constrain the latent distribution. The proposed model achieved state-of-the-art CRF representation accuracy in a number of benchmark tests, but is almost twice as fast as the best current models when performing the maximum likelihood estimation during camera response calibration due to the simple yet efficient model representation.