image processing projects for cse

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03-January-2019

 Project tunnel provide image processing projects for cse, IT, ECE, MTECH, PHD  project with source code, pdf file, ppt, project report. A Survey on various Haze Removel techniques and featues in digital image processing.Image in open environment or outdoor scenes consist of various noise in form of fog, rain, haze, etc. Due to the presence of these noise  overall quality gof the image  get degrade. As these unwanted particles in the image scattered light from the source to the object. So removal of these unwanted information is highly desired in the image because of its different requirement for analysis. This can be understand by an example that suppose one computer algorithm required image that is free from those unwanted information. So if input in such type of algorithm contain those haze, dust, etc. produce error in the image processing.

In order to create haze in the image one common formula is

I(x) = R(x)*t(x) + a*(1− t(x))

where x is a pixel location.

I is the observed image

R is the underlying scene radiance.

a is the atmospheric light (or airlight).

output. So pre-processing of such type of image is highly desired by various users of different fields. One could easily see how a car navigation system that did not take this effect into account could have dangerous consequences. Accordingly, finding effective methods for haze removal is an ongoing area of interest in the image processing and computer vision fields. Intuitively, the image received by the observer is the convex combination of an attenuated version of the underlying scene with an additive haze layer, where the atmospheric light represents the color of the haze (figure 1). The ultimate goal of haze removal is to find R, which also requires knowledge of a∞ and t. From this model, it is apparent that haze removal is an under-constrained problem. In a grayscale image, for each pixel there is only 1 constraint but 3 unknowns; for an RGB color image, there are 3 constraints but 7 unknowns (assuming t is the same for each color channel). Essentially, one must resolve the ambiguous question of whether an object’s color is a result of it being far away and mixed with haze, or if the object is close to the observer and simply the correct color.

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Customizing Deep Learning Based Facial Expression Recognition Model

An Advanced Single-Image Visibility Restoration Algorithm for Real-World Hazy Scenes

Design, Implementation, and Evaluation of a Knowledge-Based Authentication Mechanism

Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering

 

 

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