Image noise removal (denoising) is a significant preprocessing step in image processing research domain. In past decades, various noise removal algorithms have been developed, and they are categorized into conventional and deep learning denoising methods. Most recently, deep learning-based denoising methods outperform the conventional methods. However, deep learning methods have the limitations of requiring large training sample images, difficult to train the deep convolution neural network (DeCNN), and the majority of the convolutional neural network suffers from the performance saturation. Hence, using combination of block matching (BM) and dilated convolutional neural network (DCNN), a unique method for reducing noise in still images is proposed. In this approach, initially, existing algorithm used to preprocess the image. Then, BM technique uses a sliding window size of a four-by-four block and moves it across the image to select for blocks that are similar in the image. The matching blocks are highly correlated with one another. The matching blocks are then fed to the deep DCNN to remove noise and obtain a better noiseless image. Finally, the noiseless image performances are evaluated using standard metrics. From the experimental results, it is observed that the proposed BM and deep DCNN yielded substantially better results than existing state-of-the-art methods to remove the image’s noise.