Deepening Coarse-to-Fine: Evolution of Efficient Strategies for Single-Image Deblurring

In view of the wide application and remarkable results of the coarse-to-fine strategy in the field of single image deblurring, this paper discusses the optimization path of the strategy in depth, aiming at improving the efficiency of deblurring and reducing the computational redundancy. Different from the traditional method of enhancing image sharpness by stacking multi-layer networks, we innovatively reconstruct the coarse to fine processing flow and propose the Multi-Scale Feature Fusion U-NET (MSFF-Unet). The model receives multi-scale inputs through a single encoder, greatly simplifying the training process, and utilizes a shared decoder to directly generate multi-scale clear image outputs in parallel, simulating the effect of cascading U-net without the need for actual cascading structures and significantly reducing computational complexity. In addition, we introduce an asymmetric feature aggregation strategy, which effectively integrates cross-scale feature information and further enhances the deblurring effect. Comprehensive experimental verification on standard dataset such as GoPro shows that MSFF-Unet not only achieves significant improvement in image defuzzification quality, but also significantly reduces computing cost. Its performance exceeds many existing advanced technologies, demonstrating innovative potential and significant advantages in the field of image defuzzification. This research provides a new perspective and solution for building a more efficient and accurate image deblurring system.