Wholeness Unveiled: Pioneering the Fusion of Denoising and Inpainting for Mastery in Image Restoration

Effective image denoising techniques and inpainting are frequently needed for mural art restoration and preservation in order to accomplish accurate reconstruction. This comparative study examines the effectiveness of three different methods for denoising mural art images: generative adversarial network (GAN) based denoising, deep convolutional neural network (CNN) based denoising, and conventional methods like Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) techniques. Due to their extraordinary capacity to extract complex patterns and features from noisy data, deep learning approaches have recently become more popular in the field of picture denoising. In order to denoise photos of mural art, this study investigates the use of CNNs and GANs, which include a generator network and a generator network, respectively. These models will be trained and tested on a variety of noisy mural art datasets, and their denoising performance will be compared in terms of both quantitative measures and visual quality. Additionally, the study uses DWT and SWT, two conventional denoising techniques, as benchmarks. These techniques provide an alternative viewpoint and are commonly utilised in image processing. The comparison analysis takes into account elements including the effectiveness of the denoising process, the preservation of tiny details, and overall image quality. This study makes a contribution by thoroughly comparing these denoising methods in the context of restoring mural painting. Combining denoising and image inpainting techniques can be a powerful approach for restoring and enhancing images. Practitioners and researchers can choose the best denoising strategies for particular settings by carefully weighing the advantages and drawbacks of each approach. The findings of this study aim to guide the selection of appropriate methods based on the characteristics of the mural art images and the desired quality of the reconstructed results.