Dirty Pixels: Towards End-to-end Image Processing and Perception
نویسندگان
چکیده
Real-world, imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections make image processing for human viewing higher-level perception tasks challenging. Conventional cameras address this problem compartmentalizing from high-level task processing. As such, conventional involves the RAW sensor in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping, compression. This is optimized to obtain visually pleasing image. High-level processing, however, steps feature extraction, classification, tracking, fusion. While silo-ed design approach allows efficient development, it also dictates compartmentalized performance metrics without knowledge camera system. For example, today’s demosaicking denoising algorithms designed using perceptual quality but not with domain-specific object detection mind. We propose an end-to-end differentiable architecture jointly performs classification (see Figure 1). The does require any intermediate losses based on perceived learns pipelines whose outputs differ those existing ISPs quality, preserving fine detail at cost increased noise artifacts. show state-of-the-art discard information essential corner cases, extremely low-light conditions, where stacks fail. demonstrate captured simulated data our model substantially improves low light challenging which imperative real-world applications autonomous driving, robotics, surveillance. Finally, we found proposed achieves accuracy when reconstruction validating itself potentially useful drop-in network analysis beyond demonstrated work. Our models, datasets, calibration available https://github.com/princeton-computational-imaging/DirtyPixels .
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ژورنال
عنوان ژورنال: ACM Transactions on Graphics
سال: 2021
ISSN: ['0730-0301', '1557-7368']
DOI: https://doi.org/10.1145/3446918