# Deep Optics for Single-shot High-dynamic-range Imaging

## TL;DR

This paper introduces a novel deep optical imaging system that jointly optimizes a lens and neural network to achieve high-dynamic-range imaging in a single shot, outperforming existing methods.

## Contribution

It presents a new approach combining optical element design with deep learning for single-shot HDR imaging, including physical fabrication and testing.

## Key findings

- Outperforms purely CNN-based HDR methods in simulations and real-world tests.
- Joint optimization of lens PSF and neural network enhances HDR reconstruction.
- Physical prototype validates the effectiveness of the deep optical system.

## Abstract

High-dynamic-range (HDR) imaging is crucial for many computer graphics and vision applications. Yet, acquiring HDR images with a single shot remains a challenging problem. Whereas modern deep learning approaches are successful at hallucinating plausible HDR content from a single low-dynamic-range (LDR) image, saturated scene details often cannot be faithfully recovered. Inspired by recent deep optical imaging approaches, we interpret this problem as jointly training an optical encoder and electronic decoder where the encoder is parameterized by the point spread function (PSF) of the lens, the bottleneck is the sensor with a limited dynamic range, and the decoder is a convolutional neural network (CNN). The lens surface is then jointly optimized with the CNN in a training phase; we fabricate this optimized optical element and attach it as a hardware add-on to a conventional camera during inference. In extensive simulations and with a physical prototype, we demonstrate that this end-to-end deep optical imaging approach to single-shot HDR imaging outperforms both purely CNN-based approaches and other PSF engineering approaches.