For a single Lorentzian line, the corrected M-E approach has an exact analytic solution, which reveals not only how M-E performs its extension but also why it works particularly well for line shapes resulting from first-order decay processes. The corrected solution preserves these data and projects the trends established by them into the white-noise region in a model-independent way, thereby eliminating apodization and its associated errors. Although the Burg derivation begins as M-E, by making certain assumptions the Burg approach yields a procedure that deconvolves (sharpens) structure in spectra, thereby violating the basic M-E principle of leaving the low-order coefficients intact. We show that low-order coefficients can be preserved and apodization, and its associated errors eliminated with the corrected maximum-entropy (M-E) filter obtained here. In standard Fourier-space filtering, this goal cannot be achieved because of apodization, which forces a trade-off among errors arising from distortion, noise leakage, and Gibbs oscillations. In spectroscopy, the objective is to obtain information by analyzing spectra that ideally are undistorted and noise-free. The repair precision is more than 95.7% and fills in the missing information from the image, and the image quality is good. The experimental results show that this method can effectively repair the local fuzzy features of mural digital image. The depth learning model of local fuzzy feature restoration of mural digital image is obtained by using multimodal feature decomposition method, and the intelligent restoration of mural digital image is realized by this model. The two are superimposed to ensure that the image is not affected by distortion and halo, so as to ensure the clarity of image and boundary.

The mean filter template is used to restore the color of mural digital image, and the Gaussian template is used to restore the color of image details. Firstly, the digital image information of mural is collected by a scanning program, and the data in the database is extracted. In order to solve the fresco image restoration conversion and structural decomposition defects, an intelligent fresco digital image restoration technique based on machine learning algorithm is proposed. Influenced by the natural environment and preservation conditions, the frescoes in the monastery were damaged to some extent.

Krita gaussian noise reduction