Model-based multispectral texture inpainting and denoising

Visual texture inpainting and denoising aim not necessarily to recover the exact pixel-wise correspondence of the original, often unobservable, texture, but rather to reconstruct a texture that is visually indistinguishable from the original. This objective differs from standard image restoration goals and therefore may require fundamentally different restoration techniques. This work presents two multispectral texture restoration methods capable of simultaneously reducing additive Gaussian or Poisson noise and inpainting missing textural regions without visible seams or repetitions. Both methods rely on descriptive three-dimensional statistical spatial models. The first method employs a complex three-dimensional spatial Gaussian mixture model, particularly suited for regular or near-regular textures. The second method uses a causal simultaneous autoregressive model, which is more appropriate for random textures or scenarios with limited training data. Importantly, both models are inherently multispectral, enabling the restoration of even hyperspectral textures. As such, they avoid the spectral quality compromises typically encountered in many alternative approaches. The Gaussian and Poisson noise reduction achieved by the proposed method is compared with four alternative approaches, showing an average improvement of 1%–16% across the spectral range while avoiding the blurring artifacts observed in some of the other methods. • Two new random field-based generative approaches for texture inpainting and restoration. • Both methods use spatial–spectral correlations for robust denoising and large-area restoration. • They naturally generalize to hyperspectral, BTF, and dynamic textures.