Abstract
In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted lp norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the lp norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted lp norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal‐to‐noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.
Highlights
In computer vision and image processing, one of the most fundamental problems is the influence of noise
In order to evaluate the performance of the weighted lp norm sparse error constraint (WPNSEC) algorithm in gray image denoising, the peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) are used as the standard of the experimental results
An overall impression can be observed from Table 1; when the noise level increases from 20 to 50, the improvements of WPNSEC increase 0.60dB, 0.44dB, and 0.52dB on average, respectively
Summary
In computer vision and image processing, one of the most fundamental problems is the influence of noise. Zha et al [25] proposed a nonconvex lp norm minimization based similar group sparse representation model. In [27, 28], the gray image denoising algorithms were applied to three channels of the color image, respectively, whereas the key issue of this method fails to consider the correlation among the channels. It increases the time consumption and generates some wrong colors and artifacts.
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