본 연구는 차량 환경에서의 실시간 영상 처리 성능 향상을 위해 뉴럴 ISP(Neural Image Signal Processor)를 설계하고 최적화하는 방법을 제안한다. 다양한 조도와 기상 조건을 반영한 Day, Evening, Snow, Night, Night Lights 다섯 가지 환경에서 차량용 카메라 센서를 이용해 HDR RAW 데이터셋을 구축하며, 채도 보정을 가미한 전통적 파이프라인 기반의 베이스라인 ISP를통해 sRGB 타깃을 생성한다. 네트워크 구조는 MW-ISPNet을 기반으로 경량화하여 실시간성을확보하고, 색조 힌트를 통해 저조도 환경에서의 색 안정성을 높인다. 또한 전처리 레이어를 구현해 센서 입력부터 뉴럴 ISP까지의 파이프라인을 단순화하고 지연 시간을 최소화한다. 실험결과, 제안된 모델은 저조도 환경에서 기존 하드웨어 ISP 대비 높은 시각적 품질을 달성했으며, 자율주행 및 지능형 교통 시스템에 실시간 적용 가능한 수준의 성능을 확인하였다.
Keyword
뉴럴 ISP, 저조도 개선, 자율주행
KSP Keywords
Image Signal Processor(ISP)
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