Unified framework with iterative prediction for image inpainting and outpainting
Objective Image inpainting and outpainting tasks are significant challenges in the field of computer vision.They involve the filling of unknown regions in an image on the basis of information available in known regions.With its advancements,deep learning has become the mainstream approach for dealing with these tasks.However,existing solu-tions frequently regard inpainting and outpainting as separate problems,and thus,they lack the ability to adapt seamlessly between the two.Furthermore,convolutional neural networks(CNNs)are commonly used in these methods,but their limi-tation in capturing long-range content due to locality poses challenges.To address these issues,this study proposes a uni-fied framework that combines CNN and Transformer models on the basis of a divide-and-conquer strategy,aiming to deal with image inpainting and outpainting effectively.Method Our proposed approach consists of three stages:representation,prediction,and synthesis.In the representation stage,CNNs are employed to map the input images to a set of meaningful features.This step leverages the local information processing capability of CNNs and enables the extraction of relevant fea-tures from the known regions of an image.We use a CNN encoder that incorporates partial convolutions and pixel normaliza-tion to reduce the introduction of irrelevant information from unknown regions.The extracted features obtained are then passed to the prediction stage.In the prediction stage,we utilize the Transformer architecture,which excels in modeling global context,to generate predictions for the unknown regions of an image.The Transformer has been proven to be highly effective in capturing long-range dependencies and contextual information in various domains,such as natural language pro-cessing.By incorporating a Transformer,we aim to enhance the model's ability to predict accurate and coherent content for inpainting and outpainting tasks.To address the challenge of predicting features for large-range unknown regions in par-allel,we introduce a mask growth strategy.This strategy facilitates iterative feature prediction,wherein the model progres-sively predicts features for larger regions by gradually expanding the inpainting or outpainting task.This iterative process helps the model refine its predictions and capture more related contextual information,leading to improved results.Finally,we reconstruct the complete image in the synthesis stage by combining the predicted features with the known fea-tures from the representation stage.This synthesis aims to generate visually appealing and realistic results by leveraging the strengths of a CNN decoder that consists of multiple convolution residual blocks.Upsampling intervals are utilized,reduc-ing the difficulty of model optimization.Result To evaluate the effectiveness of our proposed method,we conduct compre-hensive experiments on diverse datasets that encompass objects and scenes for image inpainting and outpainting tasks.We compare our approach with state-of-the-art methods and utilize various evaluation metrics,including structural similarity index measure,peak signal-to-noise ratio,and perceptual quality metrics.The experimental results demonstrate that our unified framework surpasses existing methods across all evaluation metrics,demonstrating its superior performance.The combination of CNNs and a Transformer allows our model to capture local details and long-range dependencies,resulting in more accurate and visually appealing inpainting and outpainting results.In addition,ablation studies are conducted to con-firm the effectiveness of each component of our method,including the framework structure and the mask growth strategy.Through ablation experiments,all three stages are confirmed to contribute to performance improvement,highlighting the applicability of our method.Furthermore,we empirically investigate the effect of the head and layer numbers of the Trans-former model on overall performance,revealing that appropriate numbers of iterations,Transformer heads,and Trans-former layers can further enhance the framework's performance.Conclusion This study introduces an iterative prediction unified framework for addressing image inpainting and outpainting challenges.Our proposed method outperforms existing approaches in terms of performance,with each aspect of the design contributing to overall improvement.The combination of CNNs and a Transformer enables our model to capture the local and global contexts,leading to more accurate and visu-ally coherent image inpainting and outpainting results.These findings underscore the practical value and potential of an iterative prediction unified framework and method in the field of image inpainting and outpainting.Future research direc-tions include exploring the application of our framework to other related tasks and further optimizing the model architecture for enhanced efficiency and scalability.Moreover,an important aspect that can be explored to enhance our proposed frame-work is the integration of self-supervised learning techniques with large-scale datasets.This step can potentially improve the robustness and generalization capability of our model for image inpainting and outpainting tasks.