CURVELET TRANSFORM BASED IMAGE DE-FOCUS USING FEED FORWARD NEURAL NETWORK

ABSTRACT It is seen that pictures get degraded because of presence of noise in processes like image acquisition, storage, retrieval or transmission. With completely different sorts of noise and its extent, de-noising becomes difficult. Historically, a bunch of techniques have thought of spacial, applied mathematics and multiple domain approaches for de-noising. Yet, the scope forever exists for exploring and innovation which suggests of performing arts de-noising for enhancing image quality Within the planned work, we tend to gift ANN (Artificial Neural Network) approach to de-noise pictures by combining the options of structure separate Curvelet rework and Feed Forward Artificial Neural Network (FF-ANN). In this paper we use two techniques i.e., DWT (Discrete Wavelet Transform) and FDCT (Fast Discrete Curvelet Transform) to denoise an image. We have a tendency to apply our rule to de-noise the photographs corrupted by a form of increasing noise referred to as speckle noise. The results show that the planned methodology proves effective for a variety of variations and is appropriate for essential applications.