Multimedia sensor image detection based on constrained underdetermined equation

In order to study the image detection of multimedia sensor based on constrained under determined equation, this paper proposes the improvement of image sensor quality detection method based on image recognition algorithm. Firstly, the principle and steps of the under determined method are explained, and the complexity of the algorithm is analyzed. The image sensor is the most important part of the mobile camera, which determines the performance of the mobile camera, and its assembly quality largely determines the quality of the whole mobile camera. The case studied in this paper is model h mobile phone camera, and the first pass rate of good products is only 80%. In order to meet the 95% pass rate of good products required by customers, it is necessary to comprehensively improve the processes that have a great impact on product quality, so as to improve the first pass rate of products, achieve quality objectives and reduce manufacturing failure costs. In order to find out the threshold value of the height information measured by the improved method, to determine whether the product is good, and to find out the process that has a great impact on the surface flatness of the image sensor, the product measurement experiment is specially set. The improved war page test method was used to measure the products of each experimental group. Among them, No. 22, No. 39 and No. 40 products are poor due to changes in physical properties in subsequent production, and cannot flow to the next process for production. Therefore, the data of No. 22, No. 39 and No. 40 are missing in the test data. A total of 133 groups of experimental data were obtained in this experimental test. The results showed that the standard deviations of group 1, group 2, group 3 and group 4 were 0.172, 0.125 and 0.304 respectively. That is, after the same product is treated by false hardening, heating hardening and base installation processes, the false hardening and heating hardening processes have relatively little impact on the surface flatness of the image sensor. After the base opening and installation process, the surface flatness of the image sensor begins to change. The standard deviations of the measurement results of group 1 and groups 4, 5, 6 and 7 were 0.304, 0.381, 0.391 and 0.514 respectively. That is, after the base installation, calibration, bonding, soldering and testing of the product, the surface flatness of the image sensor and the initial surface flatness have changed greatly. Therefore, production technicians can focus on the process that starts to change, that is, the base installation process. The improved method has the advantages of automatic operation, low error, low error rate and so on.