Expanded Convolutional Neural Network Based Look-Up Tables for High Efficient Single-Image Super-Resolution

Advanced mobile computing has led to a surge in the need for practical super-resolution (SR) techniques. The look-up table (LUT) based SR-LUT has pioneered a new avenue of research without needing hardware acceleration. Nevertheless, all preceding methods that drew inspiration from the SR-LUT framework invariably resort to interpolation and rotation techniques for diminishing the LUT size, thereby prolonging the inference time and contradicting the original objective of efficient SR. Recently, a study named EC-LUT proposed an expanded convolution method to avoid interpolation operations. However, the performance of EC-LUT regarding SR quality and LUT volume is unsatisfactory. To address these limitations, this paper proposes a novel expanded convolutional neural network (ECNN). Specifically, we further extend feature fusion to the feature channel dimension to enhance mapping ability. In addition, our approach reduces the number of single indexed pixels to just one, eliminating the need for rotation tricks and dramatically reducing the LUT size from the MB level to the KB level, thus improving cache hit rates. By leveraging these improvements, we can stack expanded convolutional layers to form an ECNN, with each layer convertible to LUTs during inference. Experiments show that our method improves the overall performance of the upper limit of LUT based methods. For example, under comparable SR quality conditions, our model achieves state-of-the-art performance in speed and LUT volume.