Image Dehazing Enhancement Method Based On Improved Energy Functional And Diffusion Model

The dehazing and denoising of images have long been a research topic of great concern in the field of image processing.Dehazing and denoising techniques not only improve the quality and visual effects of images,but also assist in the accurate identification and analysis of information within images.Furthermore,these techniques facilitate the enhancement of the accuracy and robustness of image processing and analysis algorithms,mitigating issues such as misjudgment or omission caused by fog or noise in images.Thus,the dehazing and denoising techniques find wide application in areas such as computer vision,autonomous driving,and remote sensing,thus emphasizing the significant practical value of studying these techniques.This article begins by summarizing recent research progress in dehazing techniques and,taking into account the trend towards multifusion methods,classifies these techniques into four categories: scene feature driven,improved filter driven,energy functional driven,and massive data driven.By summarizing the underlying logic and mathematical models of different dehazing algorithms,this article provides a useful reference for exploring dehazing methods that balance global restoration and local characterization capabilities,and are highly interpretable.Next,based on the physical model of atmospheric scattering and an analysis of the limitations of existing dehazing algorithms,this article incorporates the impact of hardware and software components of the imaging device on image formation into the study.The impact is then integrated into the imaging model of hazy images.Using total variation as a mathematical modeling tool,this article models the impact of hardware and software components of the imaging device on image formation as image noise,constructs a dehazing model containing noise,and proves the existence of the unique optimal solution to the energy functional equation.Thus,the process of dehazing an image is transformed into a process of finding the unique optimal solution that minimizes the energy functional equation.Subsequently,an algorithm is designed to solve the minimization objective of the energy functional equation containing noise.This article then uses diffusion models,a newly emerging approach,to further improve the denoising effect of dehazed images.Considering that the diffusion model has high randomness and cannot generate images deterministically,and that dehazing itself has high control ability requirements for image generation,this article introduces structural similarity of images to improve the control ability of the diffusion model,avoiding the occurrence of random mutation phenomena during forward diffusion,and enabling the model to output the desired target image during the restoration phase.Finally,this article tests the above algorithms for dehazing and denoising effects on publicly available image datasets,and evaluates the test results based on four different metrics.The results show that the images restored by the method presented in this article have achieved good results in haze removal,noise smoothing,and detail characterization.