An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing

Abstract

In this study, a novel adaptive fuzzy weighted mean filter (AFWMF) model based on the directional median technique and fuzzy inference is presented for solving the restoring high-ratio random-valued noise in image processing. This study aims, not only to obtain information from each direction of the filtering window, but also to gain information from every pixel of the filtering windows completely. Thus, in order to implement preserving details and textures for better restoration in high-noise cases, this study utilizes the directional median to build the membership function in fuzzy inference dynamically, then calculates the weighted window corresponding to the filtering window using fuzzy inference to represent the importance of valuable pixels. Finally, the restoration pixel is calculated using the weighted window and the filtering window for the weighted mean. Subsequently, this new AFWMF model significantly improves performances in the measurement of the peak signal to noise ratio (PSNR) value for preserving detail and fixed image in noise density within the range of 20–70% for the five well-known experimental images. In extensive experiments, this study also shows the better performance of identifying the proposed peak signal-to-removal noise ratio (PSRNR) and evaluating psycho-visual tests than other listed filter methods. Furthermore, the proposed AFWMF model also has a better structural similarity index measure (SSIM) value of another indicator. Conclusively, two interesting and meaning findings are identified: (1) the proposed AFWMF model is generally the best model among the 10 listed filtering methods for image processing in terms of the measurement of two quantitative indicators for both the PSNR and SSIM values; (2) different impulse noise densities should be made for different filtering methods, and thus, this is an important and interesting issue when aiming to identify an appropriate filtering model from a variety of images for processing various noise densities.

Adaptive Noise Detector and Partition Filter for Image Restoration

“…Practically, the noise level has a great influence on the selection of noise detection threshold [12]. To make full use of the prior knowledge of image noise level, Literature [13] introduces a noise detection method based on the local consumer's index (LCI).…”

“…7, it can be found that there are still many obvious noise images in [27,35,37] and [38], while the restored image of [35] has obvious blur. The effects of [12] and [19] are relatively better, but some edges and details are not well-preserved. In contrast, the effect of the proposed filter has better visual quality.…”

“…It can be considered that for complex images with 60% RVIN, the proposed method can still detect and remove most of the random-value impulse noise and retain most of the image details. [12], (c) ROR [19], (d) DWM [27], (e) SDOOD [32], (f) SBF [35], (g) EAIF [37], (h) ASMF [38], (i) FRDFN [39], (j) EBDND [40], (k) BDND [41], (l) Proposed filter…”

“…Figure 7: Restoration results of Boat images with 60% RVIN: (a) Boat with 60% RVIN, (b) AFWMF[12], (c) ROR[19], (d) DWM[27], (e) SDOOD[32], (f) SBF[35], (g) EAIF[37], (h) ASMF[38], (i) FRDFN[39], (j) EBDND[40], (k) BDND[41], (l) Proposed filter…”