Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on the alternating direction method of multipliers and the convolution theorem. The only major difference between these methods is how they approach a convolutional least-squares fitting subproblem. This letter presents a solution to this subproblem, which improves the efficiency of the state-of-the-art algorithms. We also use the same approach for developing an efficient convolutional dictionary learning method. Furthermore, we propose a novel algorithm for convolutional sparse coding with a constraint on the approximation error.
翻译:革命稀有的编码在标准稀少近似值上有所改进,采用了全球变换变异模式。 最高效的革命稀少的编码方法基于乘数和变动定理的交替方向法。 这些方法之间唯一的主要区别是它们如何接近一个与次问题相适应的革命最小方。 这封信为这一次问题提供了一个解决方案, 提高了最先进的算法的效率。 我们还采用了同样的方法来开发一个高效的共变字典学习方法。 此外, 我们提出了一种以近似错误为制约的革命稀有编码的新算法。