提出一种基于迭代自适应滤波原理的端到端深度神经网络。该网络旨在解决由简单透镜的光学结构引起的显著图像边缘模糊问题。利用具有大视场的单个胶合透镜，提出一种像素级去模糊滤波器，该滤波器可有效地适应模糊的空间变化，从而恢复输入图像的模糊特征。通过模拟和在原型摄像机系统上进行的实验验证了所提方法的有效性。

针对苹果表层存在多种缺陷类型、对不同缺陷的检测方法不同的问题，提出一种基于改进YOLOv7-tiny的缺陷检测模型，结合相机采集的RGB+NIR多光谱图像对苹果表层多种缺陷进行了检测和分类。首先，为了提取更多有效的特征信息，提高对缺陷的定位能力，在主干网络中使用坐标注意力（CA）机制聚合坐标信息，同时在主干网络后添加上下文转换器（CoT）模块以增加全局感受野；其次，为了增强高效聚合网络的特征融合能力，将其与加权双向特征金字塔结合，调整结构中各分支的占比；最后，为了解决难易样本不均衡的问题，将损失函数更换为Focal-EIoU损失。改进后网络的平均精度均值（mAP）@0.5提升了1.2百分点，达到93.2%，识别速度为89.3 frame/s。研究结果表明，本文研究内容为苹果表层的缺陷检测提供了更加高效的方法，同时为苹果的分级提供了更加精确的依据。

红外小目标检测通常受制于较远的成像距离，使得提取目标特征成为了一种困难，如何增强目标的特征表达是近些年的主要方向之一。而过于复杂的特征表达会损失推理速度，这对于有实时性要求的红外小目标检测任务是不利的。通过使用重参数化技术结合领域中常用的残差网络作为特征提取网络，再使用额外注意力与通道注意力作为特征增强模块与特征融合模块，在数据集上取得了较好的结果。提出的模型在 SIRST 与 IRSTD-1K 数据集上分别取得了 0.734 与 0.638 的 mIoU 值，同时参数量和计算复杂度只有 0.306 M 与1.114 G FLOPs。该模型能够在推理阶段保持较少参数的同时拥有和其他领先的方法相近甚至领先的性能，在串行运行的环境上有着明显的优势。

The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.

Photoacoustic imaging (PAI) is a noninvasive emerging imaging method based on the photoacoustic effect, which provides necessary assistance for medical diagnosis. It has the characteristics of large imaging depth and high contrast. However, limited by the equipment cost and reconstruction time requirements, the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed. In this paper, a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data. Specifically, the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data, and takes the photoacoustic physical model as a prior information to guide the reconstruction process. In addition, to enhance the ability of extracting signal features, the residual block and squeeze and excitation block are introduced into the TFT-Net. For further efficient reconstruction, the final output of photoacoustic signals uses ‘filter-then-upsample’ operation with a pixel-shuffle multiplexer and a max out module. Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly, reduce background noise, and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

Limited by the dynamic range of the detector, saturation artifacts usually occur in optical coherence tomography (OCT) imaging for high scattering media. The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images. We proposed a deep learning-based inpainting method of saturation artifacts in this paper. The generation mechanism of saturation artifacts was analyzed, and experimental and simulated datasets were built based on the mechanism. Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs. The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility, strong generalization, and robustness.