Dirty Pixels: Towards End-to-End Image Processing and Perception
We propose an end-to-end architecture for joint demosaicking, denoising, deblurring, and classification that makes classification robust in low-light scenarios. The proposed architecture learns a processing pipeline optimized for classification, which enhances fine details relevant for this high-level task – at the expense of more noise as measured by conventional metrics, PSNR and SSIM – and improves state-of-the art accuracy. The proposed architecture has a principled and modular design and generalizes across light levels and cameras.
Real-world imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections that make image processing for human viewing and higher-level perception tasks challenging. Conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping, and compression. This pipeline is optimized to obtain a visually pleasing image.
High-level processing, on the other hand, involves steps such as feature extraction, classification, tracking, and fusion. While this silo-ed design approach allows for efficient development, it also dictates compartmentalized performance metrics, without knowledge of the higher-level task of the camera system. For example, today’s demosaicking and denoising algorithms are designed using perceptual image quality metrics but not with domain-specific tasks such as object detection in mind.
We propose an end-to-end differentiable architecture that jointly performs demosaicking, denoising, deblurring, tone-mapping, and classification. The architecture does not require any intermediate losses based on perceived image quality and learns processing pipelines whose outputs differ from those of existing ISPs optimized for perceptual quality, preserving fine detail at the cost of increased noise and artifacts. We show that state-of-the-art ISPs discard information that is essential in corner cases, such as extremely low-light conditions, where conventional imaging and perception stacks fail.
We demonstrate on captured and simulated data that our model substantially improves perception in low light and other challenging conditions, which is imperative for real-world applications like autonomous driving, robotics, and surveillance. Finally, we found that the proposed model also achieves state-of-the-art accuracy when optimized for image reconstruction in low-light conditions, validating the architecture itself as a potentially useful drop-in network for reconstruction and analysis tasks beyond the applications demonstrated in this work.
- Steven Diamond (Stanford University)
- Vincent Sitzmann (MIT)
- Frank Julca-Aguilar (Algolux)
- Stephen Boyd (Stanford University)
- Gordon Wetzstein (Stanford University)
- Felix Heide (Algolux)
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