基于ADMM的噪声拜耳图像联合去马赛克与去噪

Hanlin Tan, Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang
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引用次数: 47

摘要

图像去马赛克和去噪是图像信号处理的重要步骤。连续执行去马赛克和去噪具有重要的缺点,即它们会降低彼此的结果。联合去马赛克和去噪克服了在一个模型中解决这两个问题的困难。本文介绍了一种具有隐先验的统一目标函数和一种改进的ADMM,用于恢复带有噪声拜耳输入的全分辨率彩色图像。实验结果表明,我们的方法在PSNR比较和人类视觉方面都优于现有的方法。此外,该方法对噪声水平的变化具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint demosaicing and denoising of noisy bayer images with ADMM
Image demosaicing and denoising are import steps of image signal processing. Sequential executions of demosaicing and denoising have essential drawbacks that they degrade the results of each other. Joint demosaicing and denoising overcomes the difficulties by solving the two problems in one model. This paper introduces a unified object function with hidden priors and a variant of ADMM to recover a full-resolution color image with a noisy Bayer input. Experimental results demonstrate that our method performs better than state-of-the-art methods in both PSNR comparison and human vision. In addition, our method is much more robust to variations of noise level.
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