基于反馈机制的图像超分辨率重建算法

Super-Resolution Image Reconstruction Algorithm Based on Feedback Mechanism

现有的图像超分辨率重建方法充分利用了强大的深度学习模型,但忽略了人类视觉系统中普遍存在的反馈机制。提出一种新型图像超分辨率重建算法,通过具有约束条件的递归神经网络中包含的隐藏状态实现反馈机制,旨在处理网络间的反馈连接并生成更具说服力的高级表示形式,提供更多的上下文信息,从而帮助低分辨率图像完成高分辨率图像的重建。此外,具有较强早期图像重建能力的反馈网络可逐步生成最终的高分辨率图像。为解决低分辨率图像因多种类型的退化而导致的细节损失问题,引入课程学习策略,使网络适用于更复杂的任务,提升模型的鲁棒性。实验结果表明,该算法能有效提升图像超分辨率重建的准确性,与SRCNN、VDSR、RDN等算法相比,其PSNR值最高提升了7.15 dB。

Although existing super-resolution image reconstruction methods make full use of high-performance deep learning models,they ignore the feedback mechanism that is ubiquitous in the human visual system.This paper proposes a super-resolution image reconstruction algorithm based on a feedback mechanism.The feedback mechanism is implemented by using the hidden states in a recurrent neural network with constraints.For the mechanism,a feedback module is designed to process the feedback connections between networks,and generate more a more persuasive highlevel representation that provides more contextual information,which helps high-resolution image reconstruction from low-resolution images.At the same time,a feedback network with a strong ability of early image reconstruction is built.It can gradually generate the final high-resolution image.Furthermore,to address the detail loss of low-resolution images caused by multiple types of degradation,a curriculum learning strategy is introduced to make the network applicable to more complex tasks and improve its robustness.The experimental results show that the proposed algorithm effectively improves the accuracy of super-resolution image reconstruction.Its PSNR value is increased by about 0.5 compared with SRCNN,VDSR,RDN and other algorithms.