Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large since there may exist many different HR images that can be downsampled to the same LR image. As a result, it is hard to directly learn a promising SR mapping from such a large space. Second, it is often inevitable to develop very large models with extremely high computational cost to yield promising SR performance. In practice, one can use model compression techniques to obtain compact models by reducing model redundancy. Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space. To alleviate the first challenge, we propose a dual regression learning scheme to reduce the space of possible SR mappings. Specifically, in addition to the mapping from LR to HR images, we learn an additional dual regression mapping to estimate the downsampling kernel and reconstruct LR images. In this way, the dual mapping acts as a constraint to reduce the space of possible mappings. To address the second challenge, we propose a lightweight dual regression compression method to reduce model redundancy in both layer-level and channel-level based on channel pruning. Specifically, we first develop a channel number search method that minimizes the dual regression loss to determine the redundancy of each layer. Given the searched channel numbers, we further exploit the dual regression manner to evaluate the importance of channels and prune the redundant ones. Extensive experiments show the effectiveness of our method in obtaining accurate and efficient SR models.
翻译:深心神经网络在图像超分辨率(SR)任务中表现显著,通过学习从低分辨率(LR)图像到高分辨率(HR)图像的映射,在图像超清晰度(SR)任务中表现得非常出色。然而,SR问题通常是一个不恰当的问题,现有方法也会有一些局限性。首先,SR可能的映射空间可能非常大,因为可能存在许多不同的HR图像,这些图像可以降格到相同的 LR 图像。因此,很难直接从这样的大空间直接学到一个很有希望的SR(SR)回归率映射。第二,开发具有极高计算成本的非常大型模型,以产生有希望的SR(HR)图像。在实践中,人们可以使用模型压缩技术获得压缩模型模型模型的模型模型,通过减少模式冗余,而现有的方法则很难准确确定由于非常大的SR映射空间图像空间的多余部分。为了减轻第一个挑战,我们提出了双重回归学习计划计划,以缩小可能进行SR(SR)图像的缩影度。除了从LRRR到HR图像的映射外,我们还学习了额外的双重回归映射影图,我们进一步估算了另外一种双向下标的图像,以估计了SR 方向的深度的深度的深度的深度的深度测量,我们用方法来测量方法来降低的深度绘制,我们获取了方向的路径的路径的路径,从而获得了一条直路路路路路路路面的平平整。