## 1 Introduction  
When evaluating fundus images, ophthalmologists look for irregularities in the background and foreground areas. Usually, the presence of peculiar structures or conditions foretells present or impending complications [1]. The examination is painstakingly slow, and oversights may occur if there are a lot of patients to diagnose. In this case, automatic computer diagnosis can be used as a tool to support a diagnosis. The quality of fundus images is important in ensuring the effectiveness of an evaluation. In many instances, the fundus images contain contrast and luminosity variations caused by hardware and software limitations [2]. Blurring, low contrast and uneven illumination in fundus images can affect the performance of an automated system. There are many enhancement techniques for fundus images affected by contrast and luminosity variation. They are divided into three categories called spatial, frequency and deep learning approaches [3; 4]. Spatial domain methods manipulate or alter the pixels or histogram of an image directly [5; 6]. Meanwhile, frequency domain approaches use Fourier transform (FT), wavelet transform (DWT) and other transform techniques to turn an image into another domain before working on it, then turning it back to its original image space [7; 8]. Deep learning approaches work like black boxes wherein a lot of samples are required to train the networks without any specific techniques to follow [9; 10]. Many researchers utilize the green channel from RGB color images to enhance images and for further processing. In order to enhance or restore images, several adjustment methods, such as contrast-limited adaptive histogram equalization (CLAHE) and contrast normalization, have been utilized. [PERSON], [PERSON] and [PERSON] improved the contrast of retina images based on the grayscale images [11]. However, the visual color of the images changed significantly after the contrast was adjusted. Then, to restore the original color, each channel underwent adjustment, followed by a refinement step. Most papers report works that solely utilize the green channel to detect exudates, microaneurysms or hemorrhages in fundus images [12; 13]. [PERSON] et al. used the R, G, and B color channels of retina images to improve their luminosity while keeping the color information. The R, G, and B channels contain both color and luminosity information, which are correlated via their ratio. They should be adjusted to improve luminosity whilst their ratios are maintained [14]. [PERSON] et al. changed the RGB of an image into HSV space, which separated the luminance (V) from the hue (H) and saturation (S) [15]. Only the luminance of fundus images was improved using an adaptive gamma correction method. [PERSON] et al. used CIE L*a*b color space to enhance the contrast of fundus images [16]. [PERSON] et al. also presented a similar approach utilizing L*a*b color space and adopting [PERSON]'s specification [17]. Meanwhile, [PERSON] et al. converted the RGB color channel into CIECAM02 color space. They asserted that their technique showed better performance than that of histogram-based approaches [18]. [PERSON] et al. introduced a new method that utilized the three RGB channels to enhance retinal images. The green channel was Wiener-filtered and adjusted using CLAHE before being recombined with the original red and blue channels [19]. [PERSON] et al. proposed an intensity transfer strategy for the three channels [20]. [PERSON] and [PERSON] presented another approach using two color models in HSV and L*a*b with weighted average histogram equalization (WAHE) for contrast improvement [21]. Common color spaces such as RGB, HSI, HSV and L*a*b have been used to improve the luminosity and contrast of fundus images in all of the works mentioned earlier. In this study, we propose a new spatial domain approach that is designed to reduce illumination variation and enhance the contrast of fundus images affected by boundary reflectance. The method estimates the background luminosity of a fundus image via lowpass filtering.