  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.
