  The quality of images captured by smartphones is an important specification
since smartphones are becoming ubiquitous as primary capturing devices. The
traditional image signal processing (ISP) pipeline in a smartphone camera
consists of several image processing steps performed sequentially to
reconstruct a high quality sRGB image from the raw sensor data. These steps
consist of demosaicing, denoising, white balancing, gamma correction, colour
enhancement, etc. Since each of them are performed sequentially using
hand-crafted algorithms, the residual error from each processing module
accumulates in the final reconstructed signal. Thus, the traditional ISP
pipeline has limited reconstruction quality in terms of generalizability across
different lighting conditions and associated noise levels while capturing the
image. Deep learning methods using convolutional neural networks (CNN) have
become popular in solving many image-related tasks such as image denoising,
contrast enhancement, super resolution, deblurring, etc. Furthermore, recent
approaches for the RAW to sRGB conversion using deep learning methods have also
been published, however, their immense complexity in terms of their memory
requirement and number of Mult-Adds make them unsuitable for mobile camera ISP.
In this paper we propose DelNet - a single end-to-end deep learning model - to
learn the entire ISP pipeline within reasonable complexity for smartphone
deployment. Del-Net is a multi-scale architecture that uses spatial and channel
attention to capture global features like colour, as well as a series of
lightweight modified residual attention blocks to help with denoising. For
validation, we provide results to show the proposed Del-Net achieves compelling
reconstruction quality.
