Research On Single Image Reconstruction Algorithm Under Incomplete Information

Nowadays,as society enters the era of highly developed information interaction,images have become an important carrier for people to convey information.The demand for information interaction has inspired people to pursue the quality of images.However,in the process of acquiring or processing image information,it is often limited by many conditions such as the quality of the imaging device,the acquisition environment,and the transmission and storage performance of the device.The above reasons usually lead to a certain degree of image quality degradation,and ultimately obtain low-quality images.In recent years,scholars have studied how to obtain high-quality images in view of the above problems,and have achieved good results.The research theme of this paper is aimed at single image reconstruction under incomplete information.The main work and conclusions of this paper are as follows:1.Aiming at additive noise,a new method of image denoising using control system model is proposed.The model has mainly been improved in two aspects: 1)Because the image may lose data during the sampling process,which will have a serious impact on the denoised image,so this paper uses multi-packet transmission to divide the image Block splitting,used for system sampling.At the same time,the interleaving technology is introduced to disperse the error information from the local to the global image,so as to reduce the impact of data loss on the quality of the image;2)To solve the problem of restoring image texture information under incomplete image information,a new Filter,through reasonable prediction of the edges in eight directions around the noise point,to achieve the image denoising operation.The experimental results show that from the quantitative comparison results of peak signal-to-noise ratio and structural similarity,the model can recover image information better,and the model has better edge-preserving ability.2.Aiming at the problem of interpolation of uneven grid images in image superresolution reconstruction,an image super-resolution reconstruction model based on compensation algorithm is proposed.This paper introduces a span function as a criterion for edge information to the model to make up for the problem that traditional compensation algorithms cannot obtain effective image edge information.After comparing with the related methods,it is found that the improved method can effectively restore the image information without detecting the image edges in advance.Experimental data shows that the method has certain advantages in the protection of image texture information.In both subjective vision and objective evaluation indicators,this proposed improvement scheme can effectively improve image quality.