Theses and Dissertations

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Subject: search.filters.subject.Image enhancement

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    ItemOpen Access
    Improved Multi-scale Retinex For Image Enhancement Using Guided Filter And Customized Sigmoid Function With Its Implementation On FPGA
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Bhanwal, Paras; Agrawal, Yash; Khare, Manish
    Image enhancement is a technique used in digital image processing to remove or overcome the effects of noise, low illumination, blurriness, or color loss in the digital image. These effects arise during the process of image acquisition. Various other factors such as environmental conditions and data loss during image transmission can also affect the image quality. The presence of the these effects degrade the overall image quality. In application such as medical imaging, defence, aerial surveillance, traffic monitoring and others, where digital images are used for crucial purposes, it becomes very important to enhance the image before it can be used for the required purpose. In low light environmental conditions when images are acquired by camera, poor contrast and color losses can be seen in several regions of the acquired image. To enhance the image under such conditions, researchers have proposed various techniques. Some techniques produce good contrast but lacks in color reproduction, while other produces good colors along with good contrast but intensify the noise present in the dark regions of the image. In order to mitigate the issue of noise amplification while providing good color and contrast, we have proposed a retinex based image enhancement technique that uses a customized sigmoid function and guided filter for the image enhancement. We have compared the proposed method with the existing image enhancement ethods on both qualitative and quantitative basis. For qualitative analysis we have tested the proposed method for multiple images, which are obtained under different environmental conditions and in different surroundings. For quantitative analysis we have used various image quality measures such as entropy, peak signal to noise ratio and others for comparison. The proposed technique provide good contrast in the areas affected by poor contrast and produce good colors in the same. The proposed method is capable of suppressing the enhancement of noise, hence showcasing its superiority with the compared techniques.
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    ItemOpen Access
    Super-resolution of hyperspectral images
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Bhimani, Amitkumar H.; Joshi, Manjunath V.
    Hyperspectral (HS) images are used for space areal application, target detection and remote sensing application. HS images are very rich in spectral resolution but at a cost of spatial resolution. HS images generated by airborne sensors like the NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) from satellites like NASA’s Hyperion. We proposed a principal component analysis (PCA) based learning method to increase a spatial resolution of HS images. For spatial resolution enhancement of HS images we need to employ a technique to increase the resolution. We used PCA based approach by learning the details from database which consist of high spatial resolution satellite images. Super-resolution, is an ill-posed problem, and does not result to unique solution, and therefore it is necessary to regularize the solution by imposing some additional constraint to restrict the solution space. To reduce the computational complexity, minimization of the regularized cost function is done using the iterative gradient descent algorithm. In this report the effectiveness of proposed scheme is demonstrated by conducting experiments on both Multispectral (MS) and Hyperspectral real data. The HS and MS images of AVIRIS and Digital airborne Imaging spectrometer (DAIS) respectively used as input for super resolution (SR).