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SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises.
1IoT Research Division, Hyper-connected Communication Research Lab., Electronics Telecommunications Research Institute (ETRI), Daejeon 34129, Korea. sbyoo@etri.re.kr.
Sensors (Basel, Switzerland)
|April 28, 2019
Summary
This study introduces a novel deep learning network to effectively reduce complex compressed Poisson noises in images. The new method enhances image restoration performance for practical camera sensor noise challenges.
Area of Science:
- Image processing
- Computer vision
- Signal processing
Background:
- Block-based coding in real image systems often encounters Poisson noises, leading to complex compressed Poisson noises.
- Existing image restoration methods struggle with compressed Poisson noises due to inaccurate degradation models, signal-dependent noise, and intercorrelation distortion.
Purpose of the Study:
- To address the limitations of current methods in handling compressed Poisson noises.
- To propose an effective noise reduction scheme for practical image coding systems.
Main Methods:
- Developed a compressed Poisson noise corruption model.
- Proposed a secondary domain intercorrelation enhanced network.
- Combined intercorrelation prior with a deep neural network for signal-dependent noise reduction.
Main Results:
- The proposed network demonstrated superior performance compared to state-of-the-art methods.
- Evaluated on classical images, LIVE1, and SIDD datasets.
Conclusions:
- The developed network effectively reduces compressed Poisson noises.
- The approach shows significant improvements in image restoration for challenging noise conditions.
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