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首页> 外文期刊>Journal of visual communication & image representation >Image-pair-based deblurring with spatially varying norms and noisy image updating
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Image-pair-based deblurring with spatially varying norms and noisy image updating

机译：具有空间变化范数和嘈杂图像更新的基于图像对的去模糊

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This paper presents a deblurring method that effectively restores fine textures and details, such as a tree's leaves or regular patterns, and suppresses noises in flat regions using consecutively captured blurry and noisy images. To accomplish this, we used a method that combines noisy image updating with one iteration and fast deconvolution with spatially varying norms in a modified alternating minimization scheme. The captured noisy image is first denoised with a nonlocal means (NL-means) denoising method, and then fused with a deconvolved version of the captured blurred image on the frequency domain, to provide an initially restored image with less noise. Through a feedback loop, the captured noisy image is directly substituted with the initially restored image for one more NL-means denoising, which results in an upgraded noisy image with clearer outlines and less noise. Next, an alpha map that stores spatially varying norm values, which indicate local gradient priors in a maximum-a-posterior (MAP) estimation, is created based on texture likelihoods found by applying a texture detector to the initially restored image. The alpha map is used in a modified alternating minimization scheme with the pair of upgraded noisy images and a corresponding point spread function (PSF) to improve texture representation and suppress noises and ringing artifacts. Our results show that the proposed method effectively restores details and textures and alleviates noises in flat regions.
机译：本文提出了一种去模糊方法，该方法可以有效地恢复精细的纹理和细节（例如树的叶子或规则的图案），并使用连续捕获的模糊和嘈杂图像来抑制平坦区域中的噪声。为了实现这一目标，我们使用了一种方法，该方法在经过修改的交替最小化方案中结合了具有一次迭代的噪声图像更新和具有空间变化范数的快速反卷积。首先使用非局部均值（NL-means）去噪方法对捕获的噪点图像进行去噪，然后在频域上将其与反卷积的捕获模糊图像进行融合，以提供具有较少噪声的初始还原图像。通过反馈循环，将捕获的噪点图像直接替换为最初恢复的图像，以进行另一次NL均值降噪，从而产生了具有清晰轮廓和更少噪点的噪点图像。接下来，基于通过将纹理检测器应用于初始恢复的图像而发现的纹理似然度，创建一个存储空间变化范数值的alpha贴图，该范本值指示最大后验（MAP）估计中的局部梯度先验。将alpha贴图用于修改后的交替最小化方案中，该方案具有一对升级的带噪图像和相应的点扩散函数（PSF），以改善纹理表示并抑制噪声和振铃伪影。我们的结果表明，该方法有效地恢复了细节和纹理，并减轻了平坦区域的噪声。

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