상세정보
학술지
Blind Deblurring using Coupled Convolutional Sparse Coding Regularisation for Noisy-Blurry Images
- 발행일
- 201807
- 출처
- Electronics Letters, v.54 no.14, pp.874-875
- ISSN
- 0013-5194
- 출판사
- IET
- 협약과제
- 18HS1400, 운전자 주행경험 모사기반 일반도로환경의 자율주행4단계(SAE)를 지원하는 주행판단엔진 개발, 최정단
- 초록
- This Letter proposes a novel method to deblur a blurry image corrupted by noise. The authors estimate a noise-free version of the input blurred image and a corresponding noise-free version of the latent image without damaging the blur information, as well as the latent image and blur kernel in an alternating fashion. To this end, they first propose coupled convolutional sparse coding, which incorporates the coupled dictionary concept into convolutional sparse coding. Then they model the noise-free blurred image to share the sparse coefficients with the noise-free latent image using the coupled dictionaries. By utilising these noise-free images as priors in alternating latent image estimation and blur kernel estimation steps, they can estimate a high-quality latent image and blur kernel in the presence of noise. Experimental results demonstrate that the proposed method outperforms previous methods in handling noisy blurred images.
- KSP 제안 키워드
- Blurred image, Convolutional sparse coding, High-quality, Image estimation, Latent Image, blind deblurring, blur kernel estimation, coupled dictionaries, novel method, sparse coding(SC), sparse coefficients