PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

TL;DR

PyNET-CA is an improved end-to-end deep learning model for mobile RAW to RGB image reconstruction, incorporating channel attention and subpixel modules to outperform previous models in smartphone ISP tasks.

Contribution

It enhances PyNET with channel attention and subpixel modules, achieving better performance in mobile RAW to RGB image reconstruction.

Findings

Outperforms previous models in AIM 2020 challenge

Demonstrates superior image reconstruction quality

Validated through comparative experiments

Abstract

Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction. The model enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improve its performance with channel attention and subpixel reconstruction module. We demonstrate the performance of the proposed method with comparative experiments and results from the AIM 2020 learned smartphone ISP challenge. The source code of our implementation is available at https://github.com/egyptdj/skyb-aim2020-public