Pub. online:1 Jan 2017Type:Research ArticleOpen Access
Volume 28, Issue 3 (2017), pp. 505–515
Medical X-ray images are prevalent and are the least expensive diagnostic imaging method available widely. The handling of film processing and digitization introduces noise in X-ray images and suppressing such noise is an important step in medical image analysis. In this work, we use an adaptive total variation regularization method for removing quantum noise from X-ray images. By utilizing an edge indicator measure along with the well-known edge preserving total variation regularization, we obtain noise removal without losing salient features. Experimental results on different X-ray images indicate the promise of our approach. Synthetic examples are given to compare the performance of our scheme with traditional total variation and anisotropic diffusion methods from the literature. Overall, our proposed approach obtains better results in terms of visual appearance as well as with respect to different error metrics and structural similarity.
Pub. online:1 Jan 2015Type:Research ArticleOpen Access
Volume 26, Issue 4 (2015), pp. 621–634
The choice of natural image prior decides the quality of restored image. Recently successful algorithms exploit heavy-tailed gradient distribution as image prior to restore latent image with piecewise smooth regions. However, these prior assumed also remove the mid-frequency component such as textural details regions while they preserve sharp edges. That because gradient profile in fractal-like texture do not have sparse characteristic.
To restore textural features of expected latent image, in this paper, we introduce fractional-order gradient as image prior which is more appropriate to describe characteristic of image textures. From details comparison of our experiments, the textual details are more clear and visual quality is improved.
Pub. online:1 Jan 2011Type:Research ArticleOpen Access
Volume 22, Issue 3 (2011), pp. 383–394
In this paper we have proposed a novel method for image denoising using local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule. The algorithm performs separable column-wise and row-wise image denoising (i.e., independently by rows and by columns), combining the obtained results into the final image estimate. The newly developed method performs competitively among recently published state-of-the-art denoising methods in terms of the peak signal-to-noise ratio (PSNR), even outperforming them for small to medium noise variances for images that are piecewise constant along their rows and columns.