Solar images obtained from space-based platforms often suffer from degradation in the form of photon noise, sensor noise, compression artifacts, and solar granulation. These types of degradation in images affect the accuracy of automatic analysis of sunspots, active regions, and coronal structures. The current work proposes a framework for solar images denoising and restoration based on the adaptive anisotropic diffusion (AAD) method. The method adjusts the diffusion conductance parameter using local gradient statistics and neighborhood similarity measures, which allows the images to be smoothed in homogeneous areas without losing the meaningful intensity step edges. The proposed method is different from the Perona–Malik diffusion method in the sense that the structural integrity is maintained even at higher iteration counts. The proposed method has been coded in the MATLAB platform using images obtained from the Solar Dynamics Observatory (SDO). The images obtained from the Helioseismic and Magnetic Imager (HMI) and the Atmospheric Imaging Assembly (AIA) sensors are used to test the proposed method. The quantitative evaluation of the proposed method has been done using PSNR, SNR, SSIM, MSE, and Edge Preservation Index, which proves the superiority of the proposed method over conventional anisotropic diffusion. Solar image analysis and space weather monitoring applications can benefit from the proposed framework's effective preprocessing tool.