  The deep learning (DL)-based methods of low-level tasks have many advantages
over the traditional camera in terms of hardware prospects, error accumulation
and imaging effects. Recently, the application of deep learning to replace the
image signal processing (ISP) pipeline has appeared one after another; however,
there is still a long way to go towards real landing. In this paper, we show
the possibility of learning-based method to achieve real-time high-performance
processing in the ISP pipeline. We propose LW-ISP, a novel architecture
designed to implicitly learn the image mapping from RAW data to RGB image.
Based on U-Net architecture, we propose the fine-grained attention module and a
plug-and-play upsampling block suitable for low-level tasks. In particular, we
design a heterogeneous distillation algorithm to distill the implicit features
and reconstruction information of the clean image, so as to guide the learning
of the student model. Our experiments demonstrate that LW-ISP has achieved a
0.38 dB improvement in PSNR compared to the previous best method, while the
model parameters and calculation have been reduced by 23 times and 81 times.
The inference efficiency has been accelerated by at least 15 times. Without
bells and whistles, LW-ISP has achieved quite competitive results in ISP
subtasks including image denoising and enhancement.
