Model Compression and Acceleration for Single Image Super-Resolution

While deep learning-based methods for Single Image Super-Resolution (SISR) have consistently set new performance benchmarks, their substantial computational and memory footprints pose a significant barrier to deployment on resource-constrained devices.This challenge is particularly acute for SISR, a low-level vision task highly sensitive to the preservation of finegrained texture details.To bridge this critical gap, two primary strategies are investigated: the design of lightweight network architectures and the application of model compression, focusing on network pruning and parameter quantization.This paper provides a critical investigation into why general-purpose compression algorithms, often developed for high-level vision tasks, frequently yield suboptimal results for SISR.This paper analyzes the inherent challenges, such as the structural constraints imposed by residual connections on pruning and the unique statistical distributions of feature maps that complicate quantization.Ultimately, the objective of this paper is to elucidate the intricate trade-offs among reconstruction fidelity, model size, and actual on-device latency, providing a principled foundation for designing genuinely efficient SISR models for real-world applications.