Non-linear adaptive image enhancement in wireless sensor networks based on non-subsampled shearlet transform

Abstract

Acquiring clear images is a crucial precondition in many image-related applications, such as wireless sensor network, industrial inspection, and machine vision. In this paper, a multi-scale image adaptive enhancement algorithm for image sensors in wireless sensor networks based on non-subsampled shearlet transform is presented. The images are decomposed into different scales of coefficients. Then the coefficients are enhanced by a non-linear enhancement function. We set two thresholds for this function. One is used to classify the coefficients between the set to be denoised and the set to be enhanced; the other is to control the enhanced intensity of the coefficients. The thresholds are selected adaptively according to the decomposition scale and the standard deviation of the coefficients. The performance of the proposed algorithm is evaluated both objectively and subjectively. And the results show that the visibility of the images is enhanced significantly.

Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

“…The enhancement simulation results are shown in Figs. 4, 5, 6, and 7, among which (a) is the original image, (b) is enhanced using Dynamic Stretching-based Brightness Preservation [16] (DSBP), (c) is the enhancement effect via Tuned Fuzzy Intensification operators [17] (Fuzzy_INT), (d) is the image enhanced by NSCT adaptive enhancement [8], (e) is the enhancement result with NSST [10], and (f ) is the performance of the proposed algorithm.…”

“…It has good results in image processing applications, such as image fusion, enhancement, and denoising. In our early research, we have applied non-sub-sampled shearlet transform (NSST) to enhance images adaptively and fuse images in compressive domain [10,11].…”

Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

“…Moreover, owing to its high computational efficiency, the algorithm compensates for the defects of nonsubsampled contourlet transform (NSCT). At present, the NSST has been widely applied to image fusion, image denoising, and image enhancement tasks . In terms of image enhancement, although there have been some applications, its enhanced effects still have increased space, such as peak signal‐to‐noise ratio, root mean square error, and other objective indicators.…”

“…At present, the NSST has been widely applied to image fusion, [9][10][11] image denoising, [12][13][14] and image enhancement tasks. 15,16 In terms of image enhancement, although there have been some applications, its enhanced effects still have increased space, such as peak signal-to-noise ratio, root mean square error, and other objective indicators.…”

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

“…Compared to NSCT, the enhancement method based on shearlet transform (ST) has more flexible structure, higher computational efficiency and better image enhancement effect, but it does not have translation invariance. 16 Non-subsampled shearlet transform (NSST) as an improved model of ST, [17][18][19] it overcomes the Gibbs effect of ST and has superior image processing performance, but its application in image enhancement is still at the preliminary stage of exploration. In this context, a novel image enhancement approach based on NSST is proposed in this paper according to the multi-scale and multi-directional analysis characteristics of NSST theory, and compare it with the more advanced and typical enhancement methods proposed in recent years.…”