Image Denoising is a computer vision technique used to reduce unwanted noise and distortions from digital images while preserving important details such as edges, textures, and object boundaries. Noise can result from low-light conditions, sensor limitations, transmission errors, compression, or environmental interference. Denoising algorithms analyze neighboring pixels and image patterns to distinguish meaningful information from unwanted variations, producing a cleaner and more useful image.
Image Denoising is commonly used in medical imaging, photography, surveillance, satellite imagery, microscopy, and industrial inspection. Its purpose is to improve image quality and make visual information easier to analyze or interpret. Traditional filtering methods and modern deep learning models can both be used for denoising. Cleaner images can improve subsequent tasks such as image classification, object detection, segmentation, and measurement, while also helping professionals obtain more reliable visual information.
