A Temperature Noise Correction Method for CMOS Spatial Camera Using LSTM With Attention Mechanism
This study presents an innovative temperature-induced random noise correction method for complementary metal oxide semiconductor (CMOS) spatial cameras using an attention mechanism-enhanced long short-term memory (LSTM) model. The model, specifically designed to address pixel drift and random noise...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | IET Computers & Digital Techniques |
| Online Access: | http://dx.doi.org/10.1049/cdt2/6670185 |
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| Summary: | This study presents an innovative temperature-induced random noise correction method for complementary metal oxide semiconductor (CMOS) spatial cameras using an attention mechanism-enhanced long short-term memory (LSTM) model. The model, specifically designed to address pixel drift and random noise issues in CMOS space cameras due to temperature variations, incorporates a multilayer LSTM network with an attention mechanism. This study comprehensively examines the temperature-induced variations in noise characteristics of CMOS cameras across diverse thermal conditions, encompassing in-depth analyses of both dark-field and light-field scenarios. Through detailed pixel-level analysis, the study quantifies the influence of temperature on pixel values and critical performance parameters such as internal nonuniformity within the camera. The experimental results show that under the dark field condition, the fitting variance between the predicted value and the measured value ranges from 0.29585 to 5.798307. After correction in light field conditions, the average variance of images decreases to 0.29, the mean signal-to-noise ratio (SNR) increases to 80, and the photo response nonuniformity (PRNU) mean drops to 0.0161%. Compared to precorrection levels, these key metrics show significant improvements, with an average 83.57-fold reduction, 1.89-fold increase, and 84.98-fold decrease, respectively. These results confirm the effectiveness of the deep learning method in correcting temperature-induced noise, highlighting the potential for practical engineering applications. |
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| ISSN: | 1751-861X |