Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation

• Published in: Applied sciences 2023-01, Vol.13 (2), p.1034
• Main Authors: Das Mou, Trisha, Alam, Saadia Binte, Rahman, Md. Hasibur, Srivastava, Gautam, Hasan, Mahady, Uddin, Mohammad Faisal
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial environments; Back propagation; Back propagation networks; Blurring; Bone imaging; Convolution; convolutional neural network; Datasets; Decomposition; Deep learning; dual attention unit; Hypotheses; Image contrast; Image enhancement; Image processing; Information sharing; Light; Medical imaging; Methods; multi-range logarithmic transformation; Neural networks; Noise reduction; Pelvis; selective kernel feature synthesis
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13021034

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.