Deep learning-based image classification algorithms usually rely on huge amounts of training data. However， it is often difficult to obtain sufficient large-scale high-quality labeled samples in real scenarios. Aiming at the problem of insufficient generalization ability of classification models in few-shot scenarios， a few-shot image classification method based on contrast learning was proposed. Firstly， global contrast learning was added as an auxiliary target in training to enable the feature extraction network to obtain richer information from instances. Then， the query samples were split into patches and used to calculate the local contrast loss， thereby promoting the model to gain the ability to infer the global thing the local things. Finally， saliency detection was used to mix the important regions of the query samples， and complex samples were constructed， so as to improve the model generalization ability. Experimental results of 5-way 1-shot and 5-way 5-shot image classification tasks on two public datasets， miniImageNet and tieredImageNet， show that compared to the few-shot learning baseline model， Meta-Baseline， the proposed method improves the classification accuracy by 5.97 and 4.25 percentage points respectively on miniImageNet， and by 3.86 and 2.84 percentage points respectively on tieredImageNet. Besides， the classification accuracy of the proposed method on miniImageNet is improved by 1.02 and 0.72 percentage points respectively compared to that of DFR （Disentangled Feature Representation） model. It can be seen that the proposed method improves the accuracy of few-shot image classification effectively with good generalization ability.

The existing monocular depth estimation methods often use image semantic information to obtain depth， and ignore another important cue — defocus blur. At the same time， the defocus blur based depth estimation methods usually take the focal stack or gradient information as input， and do not consider the characteristics of the small variation of blur between image layers of the focal stack and the blur ambiguity on both sides of the focal plane. Aiming at the deficiencies of the existing focal stack depth estimation methods， a lightweight network based on three-dimensional convolution was proposed. Firstly， a Three-Dimensional perception module was designed to roughly extract the blur information of the focal stack. Secondly， the extracted information was concatenated with the difference features of the focal stack RGB channels output by a channel difference module to construct a focus volume that was able to identify the blur ambiguity patterns. Finally， a multi-scale three-dimensional convolution was used to predict the depth. Experimental results show that compared with methods such as All in Focus Depth Network （AiFDepthNet）， the proposed method achieves the best on seven indicators such as Mean Absolute Error （MAE） on DefocusNet dataset， and the best on four indicators as well as the suboptimal on three indicators on NYU Depth V2 dataset； at the same time， the lightweight design reduces the inference time of the proposed method by 43.92% to 70.20% and 47.91% to 77.01% on two datasets respectively. The above verifies that the proposed method can effectively improve the accuracy and inference speed of focal stack depth estimation.