Revolution: A Spatial-specific Convolution for Image Super-Resolution

Recently, deep convolution neural networks achieve remarked performance in single image super-resolution(SISR) due to their strong feature representation ability. However, most existing SR methods mainly use standard convolution in each layer, neglecting to explore the various feature of spatial domain. Standard convolution focuses on spatial invariance by using the same convolution kernel in space. It deprives kernels of the ability to adapt the different spatial positions, hence hindering the diverse of feature. Later, a new convolution operator, Involution, was proposed to implement spatial-specific convolution. But it calculated the convolution kernel parameters of surrounding pixels only by utilizing the characteristics of the central pixel, which was unreasonable and limited its performance. To address these issues, in this paper, we propose a novel convolution named Revolution, which not only incorporates spatial-specific to capture different spatial information, but also considers the relevance of the pixels to determine convolution kernel parameters to further learn more realistic feature representations. On the other hand, the Revolution we proposed makes the number of parameters smaller. Experiments demonstrate that our method obtains obvious improvement in terms of indicators and visual effects.