## 1 Introduction  
Semi dark imagery is one of the most challenging tasks to be handled by digital image comprehension, as low light covers a huge part of the image data acquisition of an environment. It has relevance to the advanced computer vision applications, such as surveillance, autonomous vehicle [1], farmland classification [2; 3], surgical instrument classification [4], medical imaging [5], wildlife tracking [6] and so on. The capability of the human brain for capturing visual information and processing it has evolved to see in the dark, whereas the self-decisive nature of the AI networks is still not equipped to process dark images [7; 8]. For the starters of digital image comprehension, the basic task is to classify the incoming image. The classification of images subjected to visibility of the content present is very crucial, as it has the potential to reject the overhead of processing images involving visual extremes. Visual extremes, in the context of content visibility, appear in the forms of bright and dark images, such images reside on the opposite sides of visual spectrums. These images are perceptually difficult to visualize and, hence, automated applications are constrained to process such images. Extracting and eliminating extremely dark and bright images will lead to the preservation of semi-dark images, having visible image features for accurate subsequent processing. Image classification in terms of visual scene visibility is accomplished by two widely used techniques, i.e., machine learning techniques and computer vision (CV) techniques. Both techniques provide limited functionality with perspective of highlighting semi dark images. The ML techniques are limited in their functional discourse due to a number of factors, ranging from data curbing nature, complexity associated with the optimizers, model overfitting issues, model training and testing time, and so on [9; 10]. Moreover, the ML methods fail to find relevance among the digital image features, i.e., same image content may appear in all the three conditions (dark, semi-dark and bright). The CV techniques recognizes problem of visibility in terms of the color model being used for image representation [11]. The visibility of an image is directly related to the luminance, which can be identified either by light sensors incorporated in the high definition cameras [7; 12; 13; 14; 15] or by manipulating the color model information. The weighted manipulation of an image's hue, saturation, lightness (HSL) color model leads to the calculation of perceived brightness/luminance of singular pixel of image. The same concept is used for identification of visibility of image. However, there is no well-defined procedure streamlined, so far, for the identification of an entire image's visibility based on luminance. Moreover, the current image classification models classify images based on luminance into two classes (dark and bright) only [2; 16]. This classification fails to highlight the semi-dark scenes, thereby a lot of images incorporating visual extremes become part of subsequent intelligent visual processing. This, eventually, increases processing costs. Unfortunately, semi-dark imagery has not received enough focus of research community as a separate entity in this domain. The image classification based on luminance should highlight semi-dark images besides bright and dark images using HSL color model.