Abstract

Recent work has shown that tailored overcomplete dictionaries can provide a better image model than standard basis functions for a variety of image processing tasks. Here we propose a modified K-SVD dictionary learning algorithm designed to maintain the advantages of the original approach but with a focus on improved convergence. We then use the learned model to denoise infrared maritime imagery and compare the performance to the original K-SVD algorithm, several overcomplete “fixed” dictionaries, and a standard wavelet denoising algorithm. Results indicate the superiority of overcomplete representations and show that our tailored approach provides similar peak signal-to-noise ratios as the traditional K-SVD at roughly half the computational cost.

Image fusion via nonlocal sparse K-SVD dictionary learning

Ying Li, Fangyi Li, Bendu Bai, and Qiang Shen
Appl. Opt. 55(7) 1814-1823 (2016)

Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm

Qianshun Liu, Jian Bai, and Feihong Yu
Appl. Opt. 53(32) 7796-7803 (2014)

Denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary

Xi Zhang, Zhongliang Li, Nan Nan, and Xiangzhao Wang
Opt. Express 30(4) 5788-5802 (2022)

Dictionary learning approach for image deconvolution with variance estimation

Hang Yang, Ming Zhu, Xiaotian Wu, Zhongbo Zhang, and Heyan Huang
Appl. Opt. 53(25) 5677-5684 (2014)

Sparsity based denoising of spectral domain optical coherence tomography images

Leyuan Fang, Shutao Li, Qing Nie, Joseph A. Izatt, Cynthia A. Toth, and Sina Farsiu
Biomed. Opt. Express 3(5) 927-942 (2012)