AI-BASED NOISE REDUCTION IN ARTISTIC PHOTOGRAPHY - Aulakh, Damanjeet; Piakaray, Mr. Divya; Singh, Priyadarshani; Barde, Saudagar Subhash; Deenadayalan T; Dash, Ipsita (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The AI-driven noise-reduction has become a game-changer in artistic photography, which allows restoring, improving, and preserving the style of a wide variety of visual fields. Conventional methods of denoising like bilateral filtering, wavelet shrinkage, and non-local means tend to be at a loss on how to trade off noise with texture especially in artistic photography where grain, contrast changes, and tonal fading hold aesthetic value. Recent developments in deep learning and, in particular, Denoising Autoencoders (DAEs), U-Net models, GAN-based image restorers, and Transformer-hybrid models, have better capabilities and learn noise patterns and aesthetic features, jointly. These models make use of massive photography data, manufactured noise (Gaussian, Poisson, noise which depends on ISO), and actual imperfection of camera sensors to create strong noise-sensitive representations. This combination of the loss of perceptual information, attention, and multi-scale features aggregation is useful to preserve trivial elements of artistic effects like brush-like textures, film-grain like textures, skin color, and fine edges. Moreover, the noise reduction of AI makes the vintage and digital artworks sound or look better by restoring damaged film photographs, enhancing the portraits in low-light conditions, and AI-assistive creative processes in digital painting, conceptual art, and stylized image generation. Quantitative measures, such as PSNR, SSIM, LPIPS, and Delta-E, show that the methods are improving steadily over the classical methods, whereas qualitative analysis reveals that the methods preserve tone and form expressive details better.