Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality

TL;DR

This paper introduces a new optimality criterion called SVE-Risk for designing spatially varying exposure multiplexing schemes in HDR imaging, demonstrating that multiplex pattern design can be decoupled from reconstruction algorithms.

Contribution

The paper proposes the SVE-Risk metric for optimal multiplex pattern design and reveals that pattern design can be separated from reconstruction, simplifying HDR imaging system development.

Findings

SVE-Risk effectively quantifies recoverable pixels in multiplexing.

Optimal multiplex patterns can be selected efficiently using the proposed algorithm.

Designs are universal and do not require joint optimization with reconstruction algorithms.

Abstract

The advancement of new digital image sensors has enabled the design of exposure multiplexing schemes where a single image capture can have multiple exposures and conversion gains in an interlaced format, similar to that of a Bayer color filter array. In this paper, we ask the question of how to design such multiplexing schemes for adaptive high-dynamic range (HDR) imaging where the multiplexing scheme can be updated according to the scenes. We present two new findings. (i) We address the problem of design optimality. We show that given a multiplex pattern, the conventional optimality criteria based on the input/output-referred signal-to-noise ratio (SNR) of the independently measured pixels can lead to flawed decisions because it cannot encapsulate the location of the saturated pixels. We overcome the issue by proposing a new concept known as the spatially varying exposure risk (SVE-Risk) which is a pseudo-idealistic quantification of the amount of recoverable pixels. We present an efficient enumeration algorithm to select the optimal multiplex patterns. (ii) We report a design universality observation that the design of the multiplex pattern can be decoupled from the image reconstruction algorithm. This is a significant departure from the recent literature that the multiplex pattern should be jointly optimized with the reconstruction algorithm. Our finding suggests that in the context of exposure multiplexing, an end-to-end training may not be necessary.