Аннотация:
The neural architecture search problem is to obtain a neural network architecture with a version of the
selected block that has the best performance according to a pre-selected evaluation strategy compared to
other alternative versions. The aim of the paper is to improve the performance of image inpainting using
neural architecture search by applying the wavelet transform to the LaMa network. Analyzing the results
of experiments on researching the performance of image inpainting using the developed software it was
noticed that the inpainting was better for images containing significant areas of uniform intensity, finegrained or structural texture. Fragments of images, including complex textures or detailed patterns were
inpainted worse. The proposed technique for searching neural architecture for image inpainting based on
LaMa differs in the ratio of image inpainting time and the quality of the reconstructed image. Inpainting
of images with large masks based on the LaMa network is improved by applying the wavelet transform.
In particular, the quality of filling the missing areas with image edges and small details is improved. In
addition, it was researched the dependence of the quality of generating of details and edges of objects in
the image on the properties of the image textures, which can be described by texture descriptors. Prospect
for further research is prediction the effectiveness of the image inpainting with the LaMa networks
depending on the estimated values of original image texture descriptors and missing areas size.
