A Image Enhancement Method For Night-Way Images

Abstract

Image processing has a wide range of applications especially in our daily lives. Image processing is not common in sensitive industrial applications. Because of these applications, very high percentage of success is requested. Also these applications work in real-time. However, it can be widely used in many daily routines (driving, entrance to the workplace/ exit, control of multimedia devices, security applications, identification applications, etc.). Especially Advanced Driver Assistance Systems (ADAS) is a popular working area for image processing. Strip tracking systems, pedestrian detection systems, reading of traffic signs and signals are based on image processing.In this study, a new method has been developed to increase the visibility levels of road images at night driving. In these images, the brightness level is low because of insufficient of light sources (headlights and road lighting) which are often used to increase the driver's view. On the other hand, adversely affects the view of driver which the headlight of coming vehicles from opposite directions, poorly structured road lighting and etc. Especially the vehicle headlights coming from the opposite direction take the eye of the drivers and cause the level of view to decrease.Intense dark areas and light sources are in the image together. By so, special to these images requires the use of an adaptive improvement method. This is because, when classical image enhancement methods are used, the visibility levels of the dark areas are increased, and the shining regions are more likely to shine and the visibility level decreases in these regions.The developed method aims at enhancement these images that drivers be exposed to. For this purpose, the light sources in the image and the magnitudes of these light sources, the distance of the pixels to be calculated from the light sources, the value of the pixel itself and the neighboring pixels are used as separate parameters. Images are enhancement with the equations developed using these parameters. When the output images obtained with the use of the developed equations and the obtained Structural Similarity İndex Maps (SSIM) are examined, it is seen that the developed method gives good results.

A new approach for SPN removal: nearest value based mean filter

“…Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Image Enhancement Factor (IEF) were used in the study to evaluate the image quality. PSNR and MSE ( Enginoğlu, Erkan & Memiş, 2019 ; Turan, 2021 ; Olmez, Sengur & Özmen Koca, 2020 ) defined as:…”

“…These are the Adaptive Frequency Median Filter (AFMF), ( Erkan et al, 2020a ), Adaptive Riesz Mean Filter (ARmF) ( Enginoğlu, Erkan & Memiş, 2019 ), Improved Adaptive Weighted Mean Filter (IAWMF) ( Erkan et al, 2020b ), Adaptive Switching Weight Mean Filter (ASWMF) ( Thanh et al, 2020a ), Adaptive Weighted Mean Filter (AWMF) ( Zhang & Li, 2014 ), Different Applied Median Filter (DAMF) ( Erkan, Gökrem & Enginoğlu, 2018 ), Iterative Mean Filter (IMF) ( Erkan et al, 2019 ), Two-Stage Filter (TSF) ( Thanh et al, 2020b ), Multistage Selective Convolution Filter (MSCF-1) ( Rafiee & Farhang, 2022 ), Different Adaptive Modified Riesz Mean Filter (DAMRmF) ( Memis & Erkan, 2021 ), Stationary Framelet Transform Based Filter (SFT_lp) ( Chen et al, 2022 ) and A New Type Adaptive Median Filter (Bilal’s Method) ( Charmouti et al, 2022 ). Results are compared by using image quality metrics: Peak Signal-to-Noise Ratio (PSNR) ( Enginoğlu, Erkan & Memiş, 2019 ; Turan, 2021 ; Olmez, Sengur & Özmen Koca, 2020 ), Image Enhancement Factor (IEF) ( Enginoğlu, Erkan & Memiş, 2019 ; Djurovi¢, 2017 ) and Structural Similarity (SSIM) ( Turan, 2021 ; Olmez, Sengur & Özmen Koca, 2020 ; Wang et al, 2004 ).…”

Enhancing the Quality of Low-Light Printed Circuit Board Images through Hue, Saturation, and Value Channel Processing and Improved Multi-Scale Retinex

“…The core concept of the Retinex theory lies in the idea that an object's color is not solely confined to the values of reflected light intensity but varies with the amount of light it receives [6]. This theory aims to ensure color consistency for objects under uneven lighting conditions, providing color constancy.…”

“…The conversion relationship can be mathematically expressed by Equations ( 4)- (6). Where (r, g, b) are the components of the corresponding color channels, and the maximum (max) and minimum (min) values of the RGB image are maintained consistently [10].…”