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CorNet: Enhancing Motion Deblurring in Challenging Scenarios Using Correlation Image Sensor

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Titel: CorNet: Enhancing Motion Deblurring in Challenging Scenarios Using Correlation Image Sensor
Autoren: Pan Wang, Toru Kurihara, Jun Yu
Quelle: IEEE Access, Vol 13, Pp 33834-33848 (2025)
Verlagsinformationen: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publikationsjahr: 2025
Schlagwörter: Correlation image sensor, deep learning, two-stream model, Electrical engineering. Electronics. Nuclear engineering, image deblurring, TK1-9971
Beschreibung: Motion deblurring in scenes involving small moving objects or low-illumination conditions is challenging. This paper presents an effective deep-learning solution that utilizes correlation images as key auxiliaries to address the problem. The correlation image, produced by a three-phase correlation image sensor (3PCIS), is a temporal correlation between incident light and reference signals within a frame time, which encodes intensity changes of incident light over the exposure time. Since correlation images explicitly record motion information lost during the blurring process during exposure, they can be used for accurately identifying the location and degree of blur, especially in low-illumination conditions and scenarios with small moving objects. Therefore, we combine correlation images and motion-blurred images as inputs and build a two-stream network for motion deblurring. Two key designs in our model are 1) Shared-gated Block (SGB), which enables information exchange between the two encoders and selectively allows useful information to pass through the network to obtain high-quality output; 2) a Motion-guided Block (MGB), decoding process that can draw more attention to the blurred areas in the image, thereby achieving clearer textures and details restoration in the blurred areas. The experimental results show that our model not only can successfully eliminate the motion blur in the above challenging scenarios, but also achieves a state-of-the-art 36.02dB in Peak Signal-to-Noise Ratio (PSNR) on the GoPro dataset with simulated correlation images.
Publikationsart: Article
ISSN: 2169-3536
DOI: 10.1109/access.2025.3543599
Zugangs-URL: https://doaj.org/article/692dee9c8557412c9ba1db01b7e10def
Rights: CC BY
Dokumentencode: edsair.doi.dedup.....73aac75cc49f44ac6893a12591efc4b8
Datenbank: OpenAIRE
Beschreibung
Abstract:Motion deblurring in scenes involving small moving objects or low-illumination conditions is challenging. This paper presents an effective deep-learning solution that utilizes correlation images as key auxiliaries to address the problem. The correlation image, produced by a three-phase correlation image sensor (3PCIS), is a temporal correlation between incident light and reference signals within a frame time, which encodes intensity changes of incident light over the exposure time. Since correlation images explicitly record motion information lost during the blurring process during exposure, they can be used for accurately identifying the location and degree of blur, especially in low-illumination conditions and scenarios with small moving objects. Therefore, we combine correlation images and motion-blurred images as inputs and build a two-stream network for motion deblurring. Two key designs in our model are 1) Shared-gated Block (SGB), which enables information exchange between the two encoders and selectively allows useful information to pass through the network to obtain high-quality output; 2) a Motion-guided Block (MGB), decoding process that can draw more attention to the blurred areas in the image, thereby achieving clearer textures and details restoration in the blurred areas. The experimental results show that our model not only can successfully eliminate the motion blur in the above challenging scenarios, but also achieves a state-of-the-art 36.02dB in Peak Signal-to-Noise Ratio (PSNR) on the GoPro dataset with simulated correlation images.
ISSN:21693536
DOI:10.1109/access.2025.3543599