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Modelling the mapping from scene irradiance to image intensity is essentialfor many computer vision tasks. Such mapping is known as the camera response.Most digital cameras use a nonlinear function to map irradiance, as measured bythe sensor to an image intensity used to record the photograph. Modelling ofthe response is necessary for the nonlinear calibration. In this paper, a newhigh-performance camera response model that uses a single latent variable andfully connected neural network is proposed. The model is produced usingunsupervised learning with an autoencoder on real-world (example) cameraresponses. Neural architecture searching is then used to find the optimalneural network architecture. A latent distribution learning approach wasintroduced to constrain the latent distribution. The proposed model achievedstate-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 themaximum likelihood estimation during camera response calibration due to thesimple yet efficient model representation.

Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network