FINO: Flow-based Joint Image and Noise Model

Abstract

One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements. To tackle such an ill-posed inverse problem, the existing denoising approaches generally focus on exploiting effective natural image priors. The utilization and analysis of the noise model are often ignored, although the noise model can provide complementary information to the denoising algorithms. In this paper, we propose a novel Flow-based joint Image and NOise model (FINO) that distinctly decouples the image and noise in the latent space and losslessly reconstructs them via a series of invertible transformations. We further present a variable swapping strategy to align structural information in images and a noise correlation matrix to constrain the noise based on spatially minimized correlation information. Experimental results demonstrate FINO's capacity to remove both synthetic additive white Gaussian noise (AWGN) and real noise. Furthermore, the generalization of FINO to the removal of spatially variant noise and noise with inaccurate estimation surpasses that of the popular and state-of-the-art methods by large margins.

References in corpus (6)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• NICE: Non-linear Independent Components Estimation
• Density estimation using Real NVP
• Guided Image Generation with Conditional Invertible Neural Networks
• Variational Denoising Network: Toward Blind Noise Modeling and Removal
• Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design