期刊
IEEE ACCESS
卷 9, 期 -, 页码 33385-33395出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3061118
关键词
Fractals; Interpolation; Noise measurement; Noise reduction; Image reconstruction; Image edge detection; Superresolution; Noisy image super-resolution; local fractal dimension; local fractal feature analysis; scaling factors
资金
- National Natural Science Foundation of China [61972227]
- Natural Science Foundation of Shandong Province [ZR2019MF051]
- Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions
The research proposes a noisy single-image super-resolution method that integrates interpolation and denoising processes under the same framework, utilizing local fractal dimension analysis for image reconstruction and denoising, resulting in a high-quality high-resolution image.
Generally, most existing super-resolution (SR) methods do not consider noise, which treats SR reconstruction and denoising as two separate problems and performs separately. However, noise is inevitably introduced in the imaging process. Based on analysis of the degraded model, in this paper, the problems of interpolation and denoising are modeled to estimate the noiseless and missing images under the same framework. By applying local fractal dimension (LFD) into image local feature analysis, a noisy single-image SR method is proposed. For each noisy image, we first construct a rational fractal interpolation model containing scaling factors, which can effectively maintain the inherent properties of the data. Furthermore, the original image structure can be well preserved by applying the interpolation model. Considering the local characteristics of the image, scaling factors are calculated on the basis of the LFDs. Then, through further local feature analysis of the interpolated image, a denoising method based on LFD is proposed for recovering a noiseless image. Finally, a high-quality high-resolution image is obtained. Experimental results demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.
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