As the revolutionary improvement being made on the performance of smartphones
over the last decade, mobile photography becomes one of the most common
practices among the majority of smartphone users. However, due to the limited
size of camera sensors on phone, the photographed image is still visually
distinct to the one taken by the digital single-lens reflex (DSLR) camera. To
narrow this performance gap, one is to redesign the camera image signal
processor (ISP) to improve the image quality. Owing to the rapid rise of deep
learning, recent works resort to the deep convolutional neural network (CNN) to
develop a sophisticated data-driven ISP that directly maps the phone-captured
image to the DSLR-captured one. In this paper, we introduce a novel network
that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to
tackle this learnable image ISP problem. By adding the wavelet transform, our
proposed method enables us to restore favorable image details from RAW
information and achieve a larger receptive field while remaining high
efficiency in terms of computational cost. The global context block is adopted
in our method to learn the non-local color mapping for the generation of
appealing RGB images. More importantly, this block alleviates the influence of
image misalignment occurred on the provided dataset. Experimental results
indicate the advances of our design in both qualitative and quantitative
measurements. The code is available publically.

本论文介绍了一种利用注意机制和小波变换，名为 AWNet 的新型网络，用于解决可学习的图像 ISP 问题。实验结果表明，该方法在 qualitatif 和 quantitatif measurements 方面都取得了进展。