2개의 가상영상을 사용하는 저조도 및 역광영상의라플라스 피라미드 융합 기법
A Laplace Pyramid Fusion Method for Low Light and Backlit Images using 2 Virtual Images
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초록
논문에서는 역광 촬영된 사진이나 저조도에서 찍힌 사진을 어두운 영역과 밝은 영역을 대표하는 두 장의 가상 영상으로 라플라시안 피라미드로 융합하여 영상의 대조비를 개선하는 방안에 대하여 소개한다. 제안된 기법은 두 장의 가상 영상을 만들 때 히스토그램 분석을 통해 히스토그램 스트레칭과 감마변환 파라미터를 영상에 따라 자동으로 조절한다. 라플라스 융합과정에서 가중치를계조값만을 사용하는 방법을 제시하고 2종의 표준 영상 데이터 세트를 사용하여 NIQA 지표를 산출하여 그 효용성을 보인다. 제안된 기법은 LUT로 구현 가능한 가상영상 생성 방법을 사용하였으며 가중치 맵 생성을 단순화하였고 1채널 융합이 가능하여 연산 부담이 적은 장점이 있다.
Abstract
This paper introduces a method to improve the contrast of images taken in backlit in low light by fusing twovirtual images representing the dark and bright areas using Laplacian pyramid. The proposed technique automaticallyadjusts histogram stretching and gamma conversion parameters according to the images through histogramanalysis when creating two virtual images. In Laplace fusion process, a method of using only grayscale values asweights is presented, and NIQA indicators were calculated using two standard image data sets to demonstrate itseffectiveness. The proposed technique uses a virtual image generation method that can be implemented as a LUT,simplifies the creation of a weight map, and has the advantage of less computational burden because one-channelfusion is possible.
참고문헌 (19)
- P. Burt, "The Laplacian Pyramid as a Compact Image Code", IEEE Transactions on Communications, 31(4), pp.532~540 (1983), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Gang, Seok-Ju, "High Dynamic Range Imaging technology and recent trends", Information Display, 20(3), pp.3~9 (2019)
- Mertens T, "Exposure fusion", Proceedings of the 15th Pacific conference on computer graphics and applications, pp.382~390 (2007)
- T. Mertens, "Exposure Fusion: A Simple and Practical Alternative to High Dynamic Range Photography", Computer Graphics Forum, 28(1), pp.161~171 (2009), Wiley[DOI]
- Se-Hwan Yun, "Image enhancement using a fusion framework of histogram equalization and laplacian pyramid", IEEE Transactions on Consumer Electronics, 56(4), pp.2763~2771 (2010), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Amina Saleem, "Image fusion-based contrast enhancement", EURASIP Journal on Image and Video Processing, 2012(1) (2012), Springer Science and Business Media LLC[DOI]
- Charles Hessel, "An Extended Exposure Fusion and its Application to Single Image Contrast Enhancement", Proc. IEEE, 2020, 137~146
- Wencheng Wang, "An Experiment-Based Review of Low-Light Image Enhancement Methods", IEEE Access, 8, pp.87884~87917 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- "matlab help center,imadjust"[URL]
- V. Bychkovsky, "Learning photographic global tonal adjustment with a database of input", output image pairs, 2011, 97~2011
- "MIT-Adobe FiveK Dataset"[URL]
- Thaweesak Trongtirakul, "Single Backlit Image Enhancement", IEEE Access, 8, pp.71940~71950 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- "Li’s Database"[URL]
- Zuiderveld K., "Graphics gems IV", pp.474~485 (1994)
- Hasler, D., "Human Vision and Electronic Imaging VIII" (2003)
- Muhammad Ali Qureshi, "Towards the design of a consistent image contrast enhancement evaluation measure", Signal Processing: Image Communication, 58, pp.212~227 (2017), Elsevier BV[DOI]
- Turgay Celik, "Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling", IEEE Transactions on Image Processing, 21(1), pp.145~156 (2012), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- A. Mittal, "Making a “Completely Blind” Image Quality Analyzer", IEEE Signal Processing Letters, 20(3), pp.209~212 (2013)
- "NIQE Software"[URL]