Objective Image deblurring is a classic low-level computer vision problem that aims to restore a sharp image from a blurry image.In recent years,convolutional neural networks(CNNs)have boosted the advancement of computer vision considerably,and various CNN-based deblurring methods have been developed with remarkable results.Although convolution operation is powerful in capturing local information,the CNNs show a limitation in modeling long-range depen-dencies.By employing self-attention mechanisms,vision Transformers have shown a high ability to model long-range pixel relationships.However,most Transformer models designed for computer vision tasks involving high-resolution images use a local window self-attention mechanism.This is contradictory to the goal of employing Transformer structures to capture true long-range pixel dependencies.We review some deblurring models that are sufficient for processing high-resolution images;most CNN-based and vision Transformer-based approaches can only extract spatial local features.Some studies obtain the information with larger receptive field by directly increasing the window size,but this method not only has exces-sive computational overhead but also lacks flexibility in the process of feature extraction.To solve the above problems,we propose a method that can incorporate local and nonlocal information for the network.Method We employ the local feature representation(LFR)modules and nonlocal feature representation(NLFR)modules to extract enriched information.For the extraction of local information,most of the existing building blocks have this capability,and we can treat these blocks directly as LFR modules.In addition to obtaining local information,we also designed a generic NLFR module that can be easily combined with the LFR module for extracting nonlocal information.The NLFR module consists of a nonlocal feature extraction(NLFE)block and an interblock transmission(IBT)mechanism.The NLFE block applies a nonlocal self-attention mechanism,which avoids the interference of local information and texture details,captures purer nonlocal infor-mation,and considerably reduces the computational complexity.To reduce the effect of accumulating more local informa-tion in the NLFE block as the network depth increases,we introduce an IBT mechanism for successive NLFE blocks,which provides a direct data flow for the transfer of nonlocal information.This design has two advantages:1)The NLFR module ignores local texture details in features when extracting information to ensure that information does not interfere with each other.2)Instead of computing the self-similarity of all pixels within the receptive field,the NLFR module adaptively samples the salient pixels,considerably reducing computational complexity.We selected LeFF and ResBlock as the LFR module combined with the NLFR module and designed two models named NLCNet_L and NLCNet_R to deal with motion blur removal and defocus blur removal,respectively,based on the single-stage UNet as the model architecture.Result We verify the gains of each component of the NLFR module in the network;the network consisting of the NLFR module com-bined with the LFR module obtains peak signal-to-noise ratio(PSNR)gains of 0.89 dB compared with using only the LFR as the building block.Applying the IBT module over this,the performance is further improved by 0.09 dB on PSNR.For fair comparisons,we build a baseline model only using ResBlock as the building block with similar computational overhead and number of parameters to the proposed network.Results demonstrate that NLFR-combined ResBlock is more effective in constructing a deblurred network than directly using ResBlock as the building block.In scalability experiments,the experi-ment shows that the combination of NLFR modules with existing building blocks can remarkably improve the deblurring per-formance,including convolutional residual blocks and a Transformer block.In particular,two networks designed with NLFR-combination LeFF block and ResBlock as the building blocks achieve excellent results in single-image motion deblurring and dual-pixel defocus deblurring compared with other methods.In accordance with a popular training method,NLCNet_L was trained on the GoPro dataset with 3 000 epochs and tested on the GoPro test set.Our method achieves the best results on the GoPro test set with the lowest computational complexity.Compared with the previous method Uformer,our method improves PSNR by 0.29 dB.We trained NLCNet_R on the DPD dataset for 200 epochs for two-pixel defocus deblurring experiments.In the combined scene category,we achieved excellent performance in all four metrics.Compared with the previous method Uformer,our method improves the PSNR in indoor and outdoor scenes by 1.37 dB and 0.94 dB,respectively.Conclusion We propose a generic NLFR module to represent the extraction of real nonlocal information from images,which can be coupled with local information within the block to improve the expressive ability of the model.Through rational design,the network composed of NLFR modules achieves excellent performance with low computational consumption,and the visual effect of the recovered image,especially the edge contours,is clearer and more complete.