The most prominent problem associated with the deconvolution layer is the
presence of checkerboard artifacts in output images and dense labels. To combat
this problem, smoothness constraints, post processing and different
architecture designs have been proposed. Odena et al. highlight three sources
of checkerboard artifacts: deconvolution overlap, random initialization and
loss functions. In this note, we proposed an initialization method for
sub-pixel convolution known as convolution NN resize. Compared to sub-pixel
convolution initialized with schemes designed for standard convolution kernels,
it is free from checkerboard artifacts immediately after initialization.
Compared to resize convolution, at the same computational complexity, it has
more modelling power and converges to solutions with smaller test errors.

提出了一种名为卷积 NN 调整大小的初始化方法，用于亚像素卷积，该方法比标准卷积内核设计的方案初始化的亚像素卷积在初始化后没有棋盘格状伪影，在相同的计算复杂性下，它具有更强的建模能力，并收敛于具有更小测试误差的解。

去除棋盘格伪影的亚像素卷积：关于亚像素卷积、调整大小卷积和卷积调整大小的注记

Checkerboard artifact free sub-pixel convolution: A note on sub-pixel  convolution, resize convolution and convolution resize

As a successful deep model applied in image super-resolution (SR), the
Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior
performance to the previous hand-crafted models either in speed and restoration
quality. However, the high computational cost still hinders it from practical
usage that demands real-time performance (24 fps). In this paper, we aim at
accelerating the current SRCNN, and propose a compact hourglass-shape CNN
structure for faster and better SR. We re-design the SRCNN structure mainly in
three aspects. First, we introduce a deconvolution layer at the end of the
network, then the mapping is learned directly from the original low-resolution
image (without interpolation) to the high-resolution one. Second, we
reformulate the mapping layer by shrinking the input feature dimension before
mapping and expanding back afterwards. Third, we adopt smaller filter sizes but
more mapping layers. The proposed model achieves a speed up of more than 40
times with even superior restoration quality. Further, we present the parameter
settings that can achieve real-time performance on a generic CPU while still
maintaining good performance. A corresponding transfer strategy is also
proposed for fast training and testing across different upscaling factors.

本研究致力于加速 Super-Resolution Convolutional Neural Network，并提出了一个紧凑的 hourglass-shape CNN 结构，以实现更快，更好的图像超分辨率，通过在网络末端添加反卷积层并缩小输入特征维度和采用更小的滤波器尺寸和更多的映射层，实现了超过 40 倍的速度提升和优越的恢复质量。