Deep Lightweight Unrolled Network for High Dynamic Range Modulo Imaging

• URL: http://arxiv.org/abs/2601.12526v1
• Date: Sun, 18 Jan 2026 18:22:38 GMT
• Title: Deep Lightweight Unrolled Network for High Dynamic Range Modulo Imaging
• Authors: Brayan Monroy, Jorge Bacca,
• Abstract summary: Modulo-Imaging (MI) offers a promising alternative for expanding the dynamic dynamic range images by resetting the signal intensity when it reaches the intensity level.<n>We introduce the Scaling Equi term that facilitates self-tuning, thereby enabling the model to adapt to new images outside the original distribution.
• Score: 19.49437461280304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modulo-Imaging (MI) offers a promising alternative for expanding the dynamic range of images by resetting the signal intensity when it reaches the saturation level. Subsequently, high-dynamic range (HDR) modulo imaging requires a recovery process to obtain the HDR image. MI is a non-convex and ill-posed problem where recent recovery networks suffer in high-noise scenarios. In this work, we formulate the HDR reconstruction task as an optimization problem that incorporates a deep prior and subsequently unrolls it into an optimization-inspired deep neural network. The network employs a lightweight convolutional denoiser for fast inference with minimal computational overhead, effectively recovering intensity values while mitigating noise. Moreover, we introduce the Scaling Equivariance term that facilitates self-supervised fine-tuning, thereby enabling the model to adapt to new modulo images that fall outside the original training distribution. Extensive evaluations demonstrate the superiority of our method compared to state-of-the-art recovery algorithms in terms of performance and quality.

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