Name: Combating Bad Weather Part II: Fog Removal from Image and Video (en Inglés)
Price: 153.32 PEN
Availability: InStock
Author: Mukhopadhyay, Sudipta ; Tripathi, Abhishek Kumar
ISBN: 9783031011245

portada Combating Bad Weather Part II: Fog Removal from Image and Video (en Inglés)

Formato

Libro Físico

Autor

Sudipta Mukhopadhyay

Abhishek Kumar Tripathi

Editorial

Springer

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 0.5 cm

Peso

0.17 kg.

ISBN13

9783031011245

N° edición

Categorías

Meteorología y climatología
Sistemas y tecnología de captación de imágenes
Reconocimiento de formas
Visión por ordenador

Combating Bad Weather Part II: Fog Removal from Image and Video (en Inglés)

Sudipta Mukhopadhyay (Autor) · Abhishek Kumar Tripathi (Autor) · Springer · Tapa Blanda

Libro Nuevo

S/ 153,32

S/ 306,65

Ahorras: S/ 153,32

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 29 de Julio y el Lunes 12 de Agosto.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Combating Bad Weather Part II: Fog Removal from Image and Video (en Inglés)"

Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execution time. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics.