Low light image denoising solution with contrast enhancement in curvelet domain using Gaussian mixture adaptation model

Images captured under low light are noisy and consist of unidentifiable features. Low light noise problem occurs in imaging devices because of smaller sensor size or insufficient exposure. Low light image denoising is an exacting task in many image processing applications. This paper proposes a patch-based image denoising method for low light images in the curvelet domain with contrast enhancement. Curvelet transform is a directional transform and it gives the best sparse representation for images with edges. Here the Expectation-Maximization (EM) algorithm, based on the Gaussian mixture adaptation method is performed in the curvelet domain for denoising. EM Algorithm helps in computing the Gaussian mixture model (GMM) parameters from the patches which are used in maximum a posteriori estimation to update them. GMM parameters and patches are updated periodically until a satisfactory result is achieved. Simulation is performed on standard test data set, and then extended to natural low light noisy images. The results of the proposed technique are then compared using quality metrics such as Peak Signal to Noise Ratio and Structural Similarity Index. It is observed that the use of curvelet transform in denoising process helps to restore the structural information satisfactorily.

Automated summary

Images captured under low light conditions often have high levels of noise, making it difficult to identify features. This paper proposes a patch-based denoising method in the curvelet domain, with contrast enhancement, to restore structural information in low light images. The use of the Expectation-Maximization algorithm with Gaussian mixture adaptation in the curvelet domain helps to achieve satisfactory results in image denoising.