# Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation
###### Abstract  
Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various Gaussian convolution kernels. In addition, backpropagation optimizes most of the parameters in this model, whereas the values in conventional models depend on an artificial environment. The model was constructed using simultaneous multi-resolution convolution and dual attention processes. We performed our experiment in the Tesla T4 GPU of Google Colab using the Customized Raw Image Dataset, College Image Dataset (CID), Extreme low-light denoising dataset (ELD), and ExDark dataset. In this approach, an extended set of features is set up to learn from several scales to incorporate contextual data. An extensive performance evaluation on the four above-mentioned standard image datasets showed that MSR-MIRNet produced standard image enhancement and denoising results with a precision of 97.33%; additionally, the PSNR/SSIM result is 29.73/0.963 which is better than previously established models (MSR, MIRNet, etc.). Furthermore, the output of the proposed model (MSR-MIRNet) shows that this model can be implemented in medical image processing, such as detecting fine scars on pelvic bone segmentation imaging, enhancing contrast for tuberculosis analysis, and being beneficial for robotic visualization in dark environments. c 2023 13 1034 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 1229 122 improving image contrast and brightness using the predicted illumination map. However, the noise in dark images is unavoidable and cannot be neglected while enhancing the image. The major limitations during image enhancement processes are color degradation, noise, blurring issues due to perceptual losses, long exposure of the camera lens, object movement, inadequate light, hardware limitations, and many others. In particular, if image enhancement is performed before image denoising, then image deblurring may occur. Some strategies directly employ the denoising procedure as a distinct component of their enhancement pipeline to suppress low-light image noise (attention-based). However, there are some contradictions when applying a cascade in image enhancement and denoising procedures. In recent years, enormous research has been done to overcome the aforementioned limitations during image enhancement. For example, [PERSON] et al. [6] proposed a model for capsule endoscopic image contrast enhancement, where the combination of two different algorithms enhanced darker parts of the endoscopy image using the weighted bilinear algorithm of half units (HWB) and the threshold weighted bilinear algorithm (TWB) which excludes excess exposure and increases specular highlight spots. In our proposed model (MSR-MIRNET) the previously established models MSR [4] and MIRNet [5] are combined, in which image denoising and enhancement are performed simultaneously with slight limitations of deblurring. The novelty of our method is that extracted features are aggregated and concatenated with a full convolution layer of the VGG16 model and upgrade the color restoration using multiple logarithmic transformations. From previous studies of some conventional methods [4; 5; 7], it has been found that these models contain limitations in terms of blurring and unrealistic color formation.