Deep multi‐level up‐projection network for single image super‐resolution

Abstract Most convolutional neural network‐based single image super‐resolution (SR) methods do not take full account of the hierarchical features of the original low‐resolution (LR) images, including the intra‐channel spatial feature information and the inter‐channel feature information, which decreases the representational capacity of the network. A deep multi‐level upprojection network (DMUN) is proposed to solve this problem. Local feature up‐projection unit is adopted in DMUN to obtain high‐resolution (HR) feature of different levels and then to reconstruct the SR image. Residual up‐projection group in DMUN mines the hierarchical LR feature information and its corresponding HR residual information recursively. A residual recorrection mechanism is further introduced, which adopts HR residual information to re‐correct HR features and enrich the details of the output image. Finally, the original residual block with spatial‐and‐channel attention mechanism is improved, which adaptively recalibrates features by considering the intra‐channel spatial relationships and the inter‐channel pixel‐wise interdependencies simultaneously. Experiments on benchmark datasets show that DMUN achieves favourable performance against state‐of‐the‐art methods.