MSSIDD: A Benchmark for Multi-Sensor Denoising
Shibin Mei, Hang Wang, Bingbing Ni

TL;DR
This paper introduces MSSIDD, a comprehensive raw-image dataset for evaluating sensor transferability in denoising models, and proposes a sensor consistency training framework to improve model generalization across different sensors.
Contribution
The paper presents the first raw-domain dataset for multi-sensor denoising and introduces a novel training framework for sensor-invariant denoising models.
Findings
Sensor transferability is crucial for mobile photography.
The proposed framework improves generalization to unseen sensors.
Experimental results validate the effectiveness of the method.
Abstract
The cameras equipped on mobile terminals employ different sensors in different photograph modes, and the transferability of raw domain denoising models between these sensors is significant but remains sufficient exploration. Industrial solutions either develop distinct training strategies and models for different sensors or ignore the differences between sensors and simply extend existing models to new sensors, which leads to tedious training or unsatisfactory performance. In this paper, we introduce a new benchmark, the Multi-Sensor SIDD (MSSIDD) dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. The MSSIDD dataset consists of 60,000 raw images of six distinct sensors, derived through the degeneration of sRGB images via different camera sensor parameters. Furthermore, we propose a sensor consistency training framework…
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Taxonomy
TopicsSensor Technology and Measurement Systems · Fault Detection and Control Systems
