Sparsity Based Poisson Denoising with Dictionary Learning

Abstract

The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive i.i.d. Gaussian noise, for which many effective algorithms are available. However, in a low SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. A recent work by Salmon et al. took this route, proposing a patch-based exponential image representation model based on GMM (Gaussian mixture model), leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR, and achieving state-of-the-art results in cases of low SNR.

13 pages, 9 figures

References in corpus (1)

• Composite Self-Concordant Minimization

Cited by in corpus (17)

• Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
• Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
• DeepRED: Deep Image Prior Powered by RED
• Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging
• Patch-Ordering as a Regularization for Inverse Problems in Image Processing
• Denoising Poisson Phaseless Measurements via Orthogonal Dictionary Learning
• Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image
• MMSE Estimation for Poisson Noise Removal in Images
• A Picture is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels
• Gaussian Process Convolutional Dictionary Learning
• Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion
• BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration
• Multiple Instance Dictionary Learning using Functions of Multiple Instances
• Class-specific Poisson denoising by patch-based importance sampling
• Image reconstruction from dense binary pixels
• Sparse and redundant signal representations for x-ray computed tomography
• Convolutional dictionary learning based auto-encoders for natural exponential-family distributions