This paper presents a novel wavelet-based approach for single-image super-resolution. Our technique integrates wavelet transform and the learned locally regularized anchored neighborhood regression model for more robust frequency estimation and image restoration. First, we decomposed the low-resolution input image into four frequency sub-bands by applying discrete wavelet transform and then processed these frequency sub-bands based on separable property of neighborhood filtering to achieve a fast parallel and vectorized operation by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. We then applied inverse discrete wavelet transform to reconstruct the original image. Super-resolution is achieved using the learned model to predict the high-resolution image features. Lastly, we explicitly unified both the locality structure and nonlocal self-similarity properties in natural image and incorporated them into our super-resolution framework to regularize the nonlinear correlation between low-resolution and high-resolution space and improve the reconstructed results. Experiments on standard images validate the effectiveness of our proposed method for effective denoising, deblurring and super-resolution reconstruction tasks compared to other top performing state-of-the-art methods both quantitatively and qualitatively.

中文翻译：

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基于可分离属性的糟糕图像数据超分辨率

本文提出了一种新颖的基于小波的单图像超分辨率方法。我们的技术将小波变换与学习的局部正则化锚定邻域回归模型相结合，以实现更强大的频率估计和图像恢复。首先，我们通过应用离散小波变换将低分辨率输入图像分解为四个频率子带，然后基于邻域滤波的可分离性对这些频率子带进行处理，从而通过减少由运算量引起的计算负担来实现快速并行和矢量化运算计算图像中每个像素的每个像素的加权函数。然后，我们应用离散离散小波逆变换来重建原始图像。使用学习的模型来预测高分辨率图像特征即可实现超分辨率。最后，我们在自然图像中明确统一了局部结构和非局部自相似属性，并将它们合并到我们的超分辨率框架中，以规范化低分辨率和高分辨率空间之间的非线性关系，并改善了重建结果。在标准图像上进行的实验证明，与定量和定性的其他性能最佳的最新技术相比，我们提出的方法能够有效地进行去噪，去模糊和超分辨率重建任务。