[논문]비간축 웨이브릿 변환과 레티넥스 기법을 이용한 HDR 업스케일링 알고리즘

한규필

doi:10.9717/kmms.2022.25.10.1395

[국내논문] 비간축 웨이브릿 변환과 레티넥스 기법을 이용한 HDR 업스케일링 알고리즘
A HDR Up-scaling Algorithm Using Undecimated Wavelet Transform and Retinex Method 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.25 no.10, 2022년, pp.1395 - 1403

한규필 (Dept. of Computer Engineering, Kumoh National Institute of Technology)

Abstract ▼ AI-Helper

Lately, over 4K high definition and high dynamic range (HDR) display devices are popularized, various interpolation and HDR methods have been researched to expand the size and the dynamic range. Since most of the legacy low resolution (LR) images require both an interpolation and a HDR tone mapping methods, the two processes should be subsequently applied. Therefore, the proposed algorithm presents a HDR up-scaling algorithm using undecimated wavelet transform and Retinex method, which transfers a LR image of low dynamic range (LDR) into the high resolution (HR) with HDR. The proposed algorithm consists of an up-scaling scheme increasing the image size and a tone mapping scheme expanding the dynamic range. The up-scaling scheme uses the undecimated version of the simplest Haar wavelet analysis for the 8-directional interpolation and the change region is extracted during the analysis. This region information is utilized in controlling the surround functions' size of the proposed tone mapping using MSRCR, to enhance the pixels of around the edges that are dominant feature of the subjective image quality. As the results, the proposed algorithm can apply an up-scaling and tone mapping processes in accordance with the type of pixel.

주제어

표/그림 (17)

그림 Fig. 1. Dynamic ranges of vision. [luminance:cd/m²].
그림 Fig. 2. Wavelet analysis. (a) 2D analysis and (b) an example.
그림 Fig. 3. 2×2 Haar WT. (a) 2×2 input image, (b) Horizontal 1D WT, and (c) Bi-directional 2D WT.
그림 Fig. 4. The relation between pair-wise differences and high frequency coefficients in Haar WT. (a) HHL, (b) HLH, and (c) HHH.
그림 Fig. 5. Block diagram of a typical MSRCR algorithm.
그림 Fig. 6. Block diagram of the proposed algorithm.
그림 Fig. 7. Nonanalytic area of conventional decimated wavelet transform. (a) LR image and (b) Interpolating pixels (H, V, D) using wavelet coefficients
그림 Fig. 8. Undecimated wavelet analysis of the proposed algorithm.
그림 Fig. 9. Phased results of the proposed interpolation. (a) 1-octave up-sampling, (b) H, V, and D directional interpolation, and (c) Residual pixel interpolation.
그림 Fig. 10. Edge and change regions. (a) Edge and (b) Change regions.
그림 Fig. 11. SSR results (W=15, 29, 61, 125).
그림 Fig. 12. Experimental images. (a) Image 1, (b) Image 2, (c) Image 3, and (d) Image 4.
표 Table 1. Experimental results of up-scaling methods.
그림 Fig. 13. Upscaling results of image 3. (a) Zero-order, (b) Bi-linear, (c) Bi-cubic, (d) The proposed, and (e) Original.
그림 Fig. 14. HDR results. (a) MSRCR and (b) MSRCR-VSF (proposed).
그림 Fig. 15. Detailed results. (a) MSRCR and (b) MSRCRVSF (proposed).
표 Table 2. Experimental parameters of MSRCR-VSF.

참고문헌 (12)

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상세보기
J.H. Kim and D.Y. Suh, "Study on the Deep Learning Based HDR Restoration Inverse Tone Mapping in Local Contrast," Proceedings of KICS 2019 Winter Conference, pp. 1269-1270, 2019.
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상세보기
K.P. Han, "A Fast MSRCR Algorithm Using Hierarchical Discrete Correlation," Journal of Korea Multimedia Society, Vol. 13, No. 11, pp. 1621-1629, 2010.
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상세보기
K.P. Han and G.H. Oh, "Adaptive Image Interpolation Using Pixel Embedding," Journal of Korea Multimedia Society, Vol. 17, No. 12, pp. 1393-1401, 2014.

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