Improving Assaulted Medical Image Quality using Improved Adaptive Filtering Network
Subhash K. Shinde, Namita Pulgam · International Journal of Intelligent Systems Technologies and Applications · 2024
Regular static convolutions work well for low-frequency information processing but fall short for high-frequency information processing.Dynamic convolution is the recent method that has spatial anisotropy and content-adaptiveness, enabling it to restore complicated and sensitive high-frequency information.The proposed method makes use of dynamic convolution to enhance the learning of multi-scale and high-frequency features.To accomplish this, two blocks -the dynamic convolution block (DCB) and the multi-scale dynamic convolution block (MDCB) are introduced.Dynamic convolution is used by the DCB to improve high-frequency information, whereas skip connections are used to protect low-frequency information.To efficiently extract multi-scale features, the MDCB uses shared adaptive dynamic kernels of increasing size along with dynamic convolution.The proposed multi-dimension feature integration mechanism is used to produce accurate and contextually enriched feature representations.For successful denoising, an improved adaptive dynamic filtering network is useful.