To address the problem of low resolution of infrared imaging system, this paper combined compression coded aperture imaging to study infrared imaging, which can break through the imaging limit of infrared detectors and achieve super-resolution imaging. Compression coded aperture imaging mainly utilizes the sparsity of images, and solves mathematical models through reconstruction algorithms and reconstructs target images with high resolution. Reconstruction algorithm is a vital procedure in the process of compression coded aperture imaging, which determines the reconstruction accuracy and reconstruction speed of the image to some extent. In this paper, the existing compression coded aperture imaging reconstruction algorithms are classified and summarized. In the infrared imaging, the typical algorithm is simulated and verified, which can provide reference for future research in the field of infrared imaging.
In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.