  Digital cameras transform sensor RAW readings into RGB images by means of
their Image Signal Processor (ISP). Computational photography tasks such as
image denoising and colour constancy are commonly performed in the RAW domain,
in part due to the inherent hardware design, but also due to the appealing
simplicity of noise statistics that result from the direct sensor readings.
Despite this, the availability of RAW images is limited in comparison with the
abundance and diversity of available RGB data. Recent approaches have attempted
to bridge this gap by estimating the RGB to RAW mapping: handcrafted
model-based methods that are interpretable and controllable usually require
manual parameter fine-tuning, while end-to-end learnable neural networks
require large amounts of training data, at times with complex training
procedures, and generally lack interpretability and parametric control. Towards
addressing these existing limitations, we present a novel hybrid model-based
and data-driven ISP that builds on canonical ISP operations and is both
learnable and interpretable. Our proposed invertible model, capable of
bidirectional mapping between RAW and RGB domains, employs end-to-end learning
of rich parameter representations, i.e. dictionaries, that are free from direct
parametric supervision and additionally enable simple and plausible data
augmentation. We evidence the value of our data generation process by extensive
experiments under both RAW image reconstruction and RAW image denoising tasks,
obtaining state-of-the-art performance in both. Additionally, we show that our
ISP can learn meaningful mappings from few data samples, and that denoising
models trained with our dictionary-based data augmentation are competitive
despite having only few or zero ground-truth labels.
