Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., L 1 or L 2 ). These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the L 2 / 3 sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The L 2 / 3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

中文翻译：

---

使用稀疏表示和颜色约束的单色图像超分辨率

基于稀疏表示模型的彩色图像超分辨率重建通常采用正则化范数（例如，L 1 或L 2 ）。这些方法在一定程度上保持图像纹理细节的能力有限，并且容易造成彩色重建图像中细节模糊和颜色伪影的问题。本文提出了一种将L 2 / 3 稀疏正则化模型与颜色通道约束相结合的颜色超分辨率重建方法。该方法将低分辨率彩色图像从 RGB 转换为 YCbCr。L 2 / 3 稀疏正则化模型旨在重建输入的低分辨率彩色图像的亮度通道。然后采用颜色通道约束方法去除重建的高分辨率图像的伪影。该方法既保证了彩色图像细节的重建质量，又提高了彩色伪影的去除能力。对自然图像的实验结果验证了我们的方法在主观和客观评价方面都有所改进。