Interstitial Lung Disease （ILD） segmentation labels are highly costly， leading to small sample sizes in existing datasets and resulting in poor performance of trained models. To address this issue， a segmentation algorithm for ILD based on multi-task learning was proposed. Firstly， a multi-task segmentation model was constructed based on U-Net. Then， the generated lung segmentation labels were used as auxiliary task labels for multi-task learning. Finally， a method of dynamically weighting the multi-task loss functions was used to balance the losses of the primary task and the secondary task. Experimental results on a self-built ILD dataset show that the Dice Similarity Coefficient （DSC） of the multi-task segmentation model reaches 82.61%， which is 2.26 percentage points higher than that of U-Net. The experimental results demonstrate that the proposed algorithm can improve the segmentation performance of ILD and can assist clinical doctors in ILD diagnosis.

The accurate estimation of the Point Spread Function (PSF) is the key point in image restoration. For the unknown PSF parameter of defocus blur, an estimation method was proposed based on blur spectrum characteristic of image edge. Specifically, the blur spectrum feature of basic edge was analyzed, and then the edge model of natural image was treated as reference image. Furthermore, the max spectrum similarity was analyzed to obtain the right parameter between the image to be restored and the blurred reference image with defocus parameter in a continuous range. The experimental results show that the proposed algorithm suits large scale defocus blur images and has strong anti-noise ability.

Fast and accurate Point Spread Function (PSF) estimation method is the premise to obtain good results on the blur image restoration. To solve the deficiency of the discrete PSF of defocus-blurred and motion-blurred images, a discretization method was proposed based on the combination of geometric property of degradation model and sub-pixel estimation. Specifically, the principle of weight allocation was defined, which was related to the distance with the neighboring pixels. Thus, the discretization of PSF was realized. Finally, the experimental results illustrate that the proposed method improves result precision and outperforms the traditional one on visual quality, sharpness evaluation function, Peak Signal-to-Noise Ratio (PSNR) and Improved Signal-to-Noise Ratio (ISNR).

Current three-dimensional reconstruction algorithms of the computer vision field have limitations that they need to deploy and calibrate the cameras around the scene, or they need a structure light. Furthermore, these algorithms are not robust enough to every object. A new kind of four camera array reconstruction algorithms which properly combined the image registration algorithm and the camera array method was proposed to solve the robustness and limitation problems. It does not need calibration or structure light support. The experiments based on complex indoor sense with shadows demonstrate that this method is able to do dense point cloud reconstruction robustly and can overcome the shortcomings of current reconstruction algorithms.