Exploring Model Depth Adaptation in Image Super-Resolution for Efficient Inference

In the field of single-image super-resolution, different images have varying levels of difficulty in restoration. The restoration for images with a significant proportion of smooth regions is easier than images with complex textures or edge areas. This implies that the restoration for easy images requires fewer computational resources, whereas the restoration for difficult images requires more. Based on this, we have designed Model Depth Adaptation SR (MDASR) network, which can adaptively adjust the capacity of the model based on the difficulty level of the input image restoration. Specifically, we have employed a dynamic structural neural network that incorporates a gating mechanism to adaptively decide whether to execute or skip the current layer based on the current input, thereby achieving adaptive modeling in deep dimension. The experimental results demonstrate that our network significantly reduces FLOPS and achieves the best LPIPS performance on all the test datasets.