Restoration of dehaze and defog image using novel cross entropy-based deep learning neural network

• Published in: Multimedia tools and applications Vol. 83; no. 20; pp. 58573 - 58606
• Main Authors: Sabitha, Ch, Eluri, Suneetha
• Format: Journal Article
• Language: English
• Published: New York Springer US 23.12.2023; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Computer vision; Cost function; Data Structures and Information Theory; Deep learning; Entropy; Fog; Haze; Image contrast; Image enhancement; Image quality; Image restoration; Light sources; Machine learning; Multimedia Information Systems; Network computers; Neural networks; Special Purpose and Application-Based Systems; Weather; Transmission map; Cross Entropy-based Deep Learning Neural Network (CE-DLNN); Dark Channel Prior (DCP); Intensity-based Dynamic Fuzzy Histogram Equalization (IDFHE); Modified Structure Transference Filtering (MST)

Computer vision applications require high-quality images with a lot of information. Images captured in poor weather conditions such as fog or haze can affect the visibility of the images, which can lead to negative interferences. Fog/haze removal is a technique that can improve image quality and res...