Date of Submission

7-28-2019

Date of Award

7-28-2020

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Subject Name

Quantitative Economics

Department

Electronics and Communication Sciences Unit (ECSU-Kolkata)

Supervisor

Chanda, Bhabatosh (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

Haze and fog are atmospheric phenomena where the particles suspended in the air obscure visibility by scattering the light propagating through the atmosphere. As a result only a part of the reflected light reaches the observer. So, the apparent intensity of the objects get reduced. Apart from that, the in-scatter of the atmospheric light creates a translucent veil, which is a common sight during haze. Image dehazing methods try to recover a haze-free version of a given image by removing the effects of haze.Although attempts have been made to accurately estimate the scene transmittance, the estimation of environmental illumination has largely been ignored. Only a few methods have been proposed for its estimation and the only the recently proposed methods have considered to estimate this when proposing an end-to-end method. So, that methods that we propose here mainly motivated by the how we can estimate the environmental illumination under different settings.We start with relaxing the haze imaging model to account for the situations when the sky is not cloudy. Normally during fog and haze the sky remains cloudy. As a result the entire scene receives the same amount of light. But the sky may not always remain cloudy when a scene is being photographed in haze or fog condition. If we only consider daytime scenes, the direct sunlight plays a role in the illumination when the sky is clear. But, when this happens, the scene receives different amount of light in different portion of the image. The imaging model is relaxed to capture this situation. The method that is proposed here is based on the color line based dehazing, extended to work under this relaxed model. Since, the proposed relaxation is done with the assumption of daytime scenes, this model is not applicable for night-time scenes. So, in the next chapter, the imaging model is further relaxed to include the night-time haze situations. This is done by allowing the environmental illumination to vary spatially within the image. But this introduces a challenge. Given a hazy image the color and even the number of different illuminants present in the scene is not known. Moreover it can vary across the scene, especially in the night-time images. We have shown the construct of color line based dehazing to estimate both the possible illuminants present in the scene and the patches they affect, by the simple technique to Hough Transform. This has enable us to propose a method that works for both day and night-time images.Although, these color line based methods works well in the default value of the parameters, its performance degrades if the parameter values are not well suited for the given image. But tuning the parameters, which are around 10 in number, is not straight forward. So Convolutional Neural Networks (CNN) are utilized in the subsequent chapters to automatically learn the haze-relevant features. In the initial attempt (Chapter 4), we work with the original imaging model (constant environmental illumination for the whole scene) and by taking small patches from the input image. The transmittance and environmental illumination is estimated from patches using a CNN based model.

Comments

ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843885

Control Number

ISILib-TH492

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

http://dspace.isical.ac.in:8080/jspui/handle/10263/2146

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