A color super-resolution with multiple nonsmooth constraints by hybrid steepest descent method
A color super-resolution with multiple nonsmooth constraints by hybrid steepest descent method
复制标题
DOI：
10.1109/icip.2005.1529886
复制
发表时间：
2005-11
期刊：
影响因子：
--
通讯作者：
Ryota Sasahara;H. Hasegawa;I. Yamada;K. Sakaniwa
中科院分区：
文献类型：
--
作者：
Ryota Sasahara;H. Hasegawa;I. Yamada;K. Sakaniwa
针对彩色高分辨率图像的彩色超分辨率恢复问题，提出了一种利用多幅低分辨率图像的知识实现彩色高分辨率图像恢复的有效方案。为了恢复视觉上自然的高分辨率图像，我们将相当光滑的初始候选对象限制为满足几个非光滑凸颜色总变化的所有边界的所有图像，以及颜色通道之间的非光滑凸相互关联度量。在提出的方案中，通过连续最小化高分辨率估计和多个低分辨率图像的低分辨率变换之间的纯均方误差的加权平均值，通过准非扩张映射的混合最陡下降法，系统地优化了所有初始候选图像的数据保真度。数值算例表明，该方案解决了恢复图像的噪声抑制和边缘保持之间的权衡问题，同时保持了信道间颜色通道间的公平互相关，恢复了视觉上自然的高分辨率图像。
An efficient scheme is presented to the color super-resolution problem for recovery of a color high-resolution image with knowledge of multiple Bayer filtered low-resolution images. To recover a visually natural high-resolution image, we restrict fairly smooth initial candidates to all images satisfying all bounds imposed on the several nonsmooth convex color total variations as well as a non-smooth convex inter cross correlation measure among color channels. In the proposed scheme, the data-fidelity is optimized in a systematic way, over all initial candidates, with the hybrid steepest descent method for quasi-nonexpansive mappings [Yamada & Ogura 2004], by minimizing successively an weighted average of pure mean square errors between the low-resolution transforms of the high-resolution estimate and the multiple low-resolution images. Numerical examples show that the proposed scheme recovers visually natural high resolution images by resolving the tradeoff between noise suppression and edge preservation of the recovered image while keeping fair inter channel cross correlation among color channels.