IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder

The effective restoration of degraded remote sensing images is one of the major concerns as it directly affects imaging system performance. In recent years, investigators have developed innumerable systems and methods for remotely sensed hazy or blurred image restoration to enhance the system’s performance. However, the existing remotely sensed image restoration system and methods still have untold limitations such as texture degradation, high computational complexity, preparative parametric tunning, more time consumption in restoration procedure, lesser accuracy, and many more. Hence, effective and fast restoration of the degraded remote sensing images with more accuracy is still a challenging problem, which requires enormous attention toward a novel image restoration approach. In this work, the researchers implement a remote sensing image restoration framework namely IMU-CNN, which is based on mask-upgraded cascade R-CNN and deep autoencoder. Our proposed IMU-CNN model performs hazy or blurred remotely sensed image restoration in a faster manner with improved accuracy, as well as saves time in the image restoration process, significantly. The outcome of the proposed image restoration framework is found improved and optimal and measured accuracy, precision, F1 score, and Recall is 99.59%, 97.24%, 97.69%, and 96.49%, respectively. Recently, researchers have conducted much investigation on remotely sensed image restoration systems by using various hybrid approaches. However, still, there is a need for further investigation in the future for building sophisticated and less computationally complex systems for effective remotely sensed image restoration in less time.