DIFAR: Deep Image Formation and Retouching

11/29/2019 ∙ by Sean Moran, et al. ∙ 1

We present a novel neural network architecture for the image signal processing (ISP) pipeline. In a camera system, the ISP is a critical component that forms a high quality RGB image from RAW camera sensor data. Typical ISP pipelines sequentially apply a complex set of traditional image processing modules, such as demosaicing, denoising, tone mapping, etc. We introduce a new deep network that replaces all these modules, dubbed Deep Image Formation And Retouching (DIFAR). DIFAR introduces a multi-scale context-aware pixel-level block for local denoising/demosaicing operations and a retouching block for global refinement of image colour, luminance and saturation. DIFAR can also be trained for RGB to RGB image enhancement. DIFAR is parameter-efficient and outperforms recently proposed deep learning approaches in both objective and perceptual metrics, setting new state-of-the-art performance on multiple datasets including Samsung S7 and MIT-Adobe 5k.

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