Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

Abstract

Surveillance videos of coal mining faces have close relation to the safety of coal miners and mining efficiency. However, surveillance videos are always disturbed by some severe conditions such as atomization, low illumination, glare, and so on. Therefore, this paper proposed a hybrid algorithm (SSR-BF) based on the integration of single-scale Retinex (SSR) and bilateral filtering (BF) to enhance the image quality of surveillance videos. BF was coupled with SSR to reduce the noises and perfect the edge information in the image. The schematic diagram and pseudocode of SSR-BF was designed, and the parameters were set rationally to ensure the enhancement effects through some simulations. Finally, some comparisons with other methods were carried out, and the simulation results demonstrated that the proposed algorithm was superior to others and could be applied to image enhancement for poormonochrome images, especially the surveillance video of a coal mining face.

Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

“…To further verify the advantages of the proposed method, the performance of the proposed method was compared with two histogram based techniques, HE [8] and CLAHE [11]; three Retinex theory based techniques, SSR [13], MSR [17] and SSR + BF [14]; and two transform domain based techniques, HF [19] and UIC [21]. The most representative image was selected from each type of image (four types) as the test image for further analysis.…”

“…In this article, some of the images in the LIVE database are used as training sample. As different methods score the image quality in different ways, and the order of magnitude is different, the image quality ratio R is adopted to verify the effectiveness of the method proposed in this paper, which is represented by Q After Befor Q R  (14) where QBefor and QAfter represent the pre-enhancement and post-enhancement image quality scores under the same image quality assessment method, respectively. The results are shown in the following figures.…”

Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

“…Visual inspection has been extensively investigated and is a well-established approach for a myriad of engineering applications. In dynamic scenes, visual inspection helps to improve the efficiency of monitoring tasks, but low visual quality may undermine its effectiveness [1][2][3][4]. For instance, in manufacturing environments, the object under inspection moves along the assembly line during exposure time, resulting in motion blur.…”

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

“…On the other hand, most haze removal research assumes that the atmospheric light intensity is uniform, and A is estimated as a constant. According to Equation (9), if the input image has sufficient illumination, which means A = 255 = U J = A J , the proposed model is equivalent to the dehaze model. Nevertheless, the light intensity of a real scene is always nonuniform.…”

“…Therefore, Jobson continued his research and proposed the MSR (multiscale Retinex) algorithm [5], which has been the most widely used in recent years. Most improved Retinex algorithms [6][7][8][9][10][11][12][13][14][15] are based on MSR. However, the Gaussian filtering used by the MSR algorithm calculates a large number of floating data, which makes the algorithm take too much time.…”