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

• URL: http://arxiv.org/abs/2306.17367v1
• Date: Fri, 30 Jun 2023 02:08:25 GMT
• Title: Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality
• Authors: Xiangyu Qu, Yiheng Chi, Stanley H. Chan
• Abstract summary: We propose a new concept known as the spatially varying exposure risk (SVE-Risk) which is a pseudo-idealistic enumeration of the amount of recoverable pixels. We show that given a multiplex pattern, the conventional optimality criteria based on the input/output-referred signal-to-noise ratio can lead to flawed decisions. We report a design observation that the design universality pattern can be decoupled from the image reconstruction algorithm.
• Score: 15.525314212209564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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