S ³ learning: image enhancement using optimized very deep homomorphic filtering enabled image enhancer

The image enhancement process is widely used to standardize the visual quality of the image by improving the brightness, sharpness, and contrast, significantly. Because of this effectiveness, several researchers have designed various techniques to perform the image enhancement process. However, these methods ended with certain challenges, as background noises, computational issues, reduced image dimensionality, and missing image beneficial features. To tackle these difficulties, a proposed model is designed as an optimized very Deep Homomorphic filtering-enabled Image enhancer method (S3 Learning with DHI enhancer) for effective image enhancement. Moreover, the proposed model preserves the sharpness and edge features significantly and smoothens the image with minimal cost. From this perspective, the model corrects the parameter of the phenotypic information of imaging features, which enhances the image effectively. Moreover, the stability and accurateness of the enhancement process are improved by the implementation of the developed S3 Learning optimization algorithm. Based on these estimations, the performance effectiveness of the model is evaluated by specific estimation measures, which obtain the value of 1.788 for Mean Square Error (MSE), 0.82 for Structural Similarity Index Measure (SSIM), 52.86 dB of Peak Signal-to-Noise Ratio, and 0.2. for Blind/Reference less Image Spatial Quality Evaluator (BRISQUE).