<p>Satellite images that are transmitted from space to the ground can become distorted due to various types of noises and the image quality may be degraded. Satellite image denoising and enhancement is the initial process of satellite image processing which has a high influence on the final prediction outcomes. Efficient denoising and enhancement are important to improve satellite image quality. Conventional denoising and image enhancement techniques can eliminate noise, but they are unable to maintain image quality and cause excessive edge blurring. To tackle these challenges, this work presents a noise removal and contrast enhancement process in the satellite images. The deep learning model Denoised Attention-Convolutional Neural Network (DA-CNN) is presented to denoise and Reformed Histogram Equalization (ORHE) to enhance the satellite images. The DA-CNN is presented for learning and removing noise patterns. Then, the ORHE is used for enhancing brightness and contrast for better visualization. The color enhancement process utilizes Improved Shark Smell Optimization (ISSO) for optimizing the parameter by considering Gray Level Co-Occurrence Matrix (GLCM), edge and entropy values. The experimentation is carried out in the Kaggle dataset and Realtime Datasets (India_ISRO) achieved better PSNR Values of 44.51 &amp; 41.56 and MSE values of 0.0155and 0.02 respectively. The findings demonstrate that the suggested framework performs better than all other methods, both in terms of qualitative and quantitative analysis.</p>