论文标题
基于加权编码的图像插值与非局部线性回归模型
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model
论文作者
论文摘要
图像插值是图像超分辨率的一种特殊情况，其中低分辨率图像被直接从其高分辨率对应物中直接采样而不会模糊和噪声。因此，超分辨率模型中采用的假设对于图像插值无效。为了解决这个问题，我们提出了一个基于稀疏表示形式的新型图像插值模型。两个广泛使用的先验，包括稀疏性和非本地自相似性，用作增强插值模型稳定性的正规化术语。同时，我们将非局部线性回归纳入该模型，因为非局部相似的贴片可以为给定的贴片提供更好的近似值。此外，我们提出了一种新的方法来在线学习自适应量词，而不是聚类。对于每个贴片，将类似的贴片分组以学习自适应量词，从而产生更稀疏，更准确的表示。最后，引入了加权编码以抑制数据保真度中拟合残差的尾巴。丰富的实验结果表明，我们所提出的方法在定量措施和视觉质量方面优于几种最新方法。
Image interpolation is a special case of image super-resolution, where the low-resolution image is directly down-sampled from its high-resolution counterpart without blurring and noise. Therefore, assumptions adopted in super-resolution models are not valid for image interpolation. To address this problem, we propose a novel image interpolation model based on sparse representation. Two widely used priors including sparsity and nonlocal self-similarity are used as the regularization terms to enhance the stability of interpolation model. Meanwhile, we incorporate the nonlocal linear regression into this model since nonlocal similar patches could provide a better approximation to a given patch. Moreover, we propose a new approach to learn adaptive sub-dictionary online instead of clustering. For each patch, similar patches are grouped to learn adaptive sub-dictionary, generating a more sparse and accurate representation. Finally, the weighted encoding is introduced to suppress tailing of fitting residuals in data fidelity. Abundant experimental results demonstrate that our proposed method outperforms several state-of-the-art methods in terms of quantitative measures and visual quality.
