Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

Smoothing and noise reduction of images is often an important first step in image processing applications. Simple image smoothing algorithms like the Gaussian filter have the unfortunate side effect of blurring the image which could obfuscate important information and have a negative impact on the following applications. The bilateral filter is a well-used non-linear smoothing algorithm that seeks to preserve edges and contours while removing noise. The bilateral filter comes at a heavy cost in computational speed, especially when used on larger images, since the algorithm does a greater amount of work for each pixel in the image than some simpler smoothing algorithms. In applications where timing is important, this may be enough to encourage certain developers to choose a simpler filter, at the cost of quality. However, the time cost of the bilateral filter can be greatly reduced through parallelization, as the work for each pixel can theoretically be done simultaneously. This work uses Nvidia’s Compute Unified Device Architecture (CUDA) to implement and evaluate some of the most common and effective methods for parallelizing the bilateral filter on a Graphics processing unit (GPU). This includes use of the constant and shared memories, and a technique called 1 x N tiling. These techniques are evaluated on newer hardware and the results are compared to a sequential version, and a naive parallel version not using advanced techniques. This report also intends to give a detailed and comprehensible explanation to these techniques in the hopes that the reader may be able to use the information put forth to implement them on their own. The greatest speedup is achieved in the initial parallelizing step, where the algorithm is simply converted to run in parallel on a GPU. Storing some data in the constant memory provides a slight but reliable speedup for a small amount of work. Additional time can be gained by using shared memory. However, memory transactions did not account for as much of the execution time as was expected, and therefore the memory optimizations only yielded small improvements. Test results showed 1 x N tiling to be mostly non-beneficial for the hardware that was used in this work, but there might have been problems with the implementation.

Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

No description available.

No description available.

Many applications require automatic image analysis for different quality of the input images. In many cases, the quality of acquired images is suitable for the purpose of the application. However, in some cases the quality of the acquired image has to be modified according to needs of a specific application. A higher quality of the image can be achieved by Image Enhancement (IE) algorithms. The choice of IE technique is challenging as this choice varies with the application purpose. The goal of this research is to investigate the possibility of the selective application for the IE algorithms. The values of entropy and Peak Signal to Noise Ratio (PSNR) of the acquired image are considered as parameters for selectivity. Three algorithms such as Retinex, Bilateral filter and Bilateral tone adjustment have been chosen as IE techniques for evaluation in this work. Entropy and PSNR are used for the performance evaluation of selected IE algorithms. In this study, we considered the images from three fingerprint image databases as input images to investigate the algorithms. The decision to enhance an image in these databases by the considered algorithms is based on the empirically evaluated entropy and PSNR thresholds. Automatic Fingerprint Identification System (AFIS) has been selected as the application of interest. The evaluation results show that the performance of the investigated IE algorithms affects significantly the performance of AFIS. The second conclusion is that entropy and PSNR might be considered as indicators for required IE of the input image for AFIS.

Operadores de tone mapping reverso (RTMOs) realçam imagens e vídeos de baixa faixa dinâmica para visualização em monitores de alta faixa dinâmica. Um problema comum encontrado por operadores anteriores é a maneira com que tratam conteúdo sub ou superexposto. Sob tais condições, eles podem não ser eficientes, e até mesmo causar perda e reversão de contraste visível. Apresentamos uma classe de operadores de tone mapping reverso, baseados no filtro bilateral cruzado (cross bilateral filter), capazes de gerar imagens HDR de alta qualidade. Experimentos utilizando uma métrica objetiva de avaliação de imagens demostram que nosso método é a única técnica capaz de realçar detalhes perceptíveis ao longo de uma grande gama de exposições de imagem, a qual inclui desde imagens subexpostas até imagens superexpostas. / Reverse tone mapping operators (rTMOs) enhance low-dynamic-range images and videos for display on high dynamic range monitors. A common problem faced by previous rTMOs is the handling of under or overexposed content. Under such conditions, they may not be effective, and even cause loss and reversal of visible contrast. We present a class of local rTMOs based on cross bilateral filtering that is capable of generating highquality HDR images and videos for a wide range of exposure conditions. Experiments performed using an objective image quality metric show that our approach is the only single technique available that can gracefully enhance perceived details across a large range of image exposures.

In this thesis, a single image dehazing method based on modified dark channel prior and haze (fog) density detection is proposed. Dark channel prior dehazing algorithm is achieved good results for some haze images. However, we observed that haze images contain low and high haze density. Thus, the region of low haze density is unnecessary to dehaze. To solve this problem, we first defined the HSV distance, pixel-based dark channel prior and pixel-based bright channel prior to estimate the haze density. Further to enhance the dehazing performance of dark channel prior, the atmospheric light value and dehazing weighting is revised based on the HSV distance. Then the new transmission map is obtained. After that, a bilateral filter is applied to refine the transmission map, which can provide the higher accuracy of transmission map. Finally, the haze-free image is recovered by combining the input image and the refined transmission map. As a result, high-quality haze-free image can be recovered with lower computational complexity, which can be naturally extended to video dehazing.

Operadores de tone mapping reverso (RTMOs) realçam imagens e vídeos de baixa faixa dinâmica para visualização em monitores de alta faixa dinâmica. Um problema comum encontrado por operadores anteriores é a maneira com que tratam conteúdo sub ou superexposto. Sob tais condições, eles podem não ser eficientes, e até mesmo causar perda e reversão de contraste visível. Apresentamos uma classe de operadores de tone mapping reverso, baseados no filtro bilateral cruzado (cross bilateral filter), capazes de gerar imagens HDR de alta qualidade. Experimentos utilizando uma métrica objetiva de avaliação de imagens demostram que nosso método é a única técnica capaz de realçar detalhes perceptíveis ao longo de uma grande gama de exposições de imagem, a qual inclui desde imagens subexpostas até imagens superexpostas. / Reverse tone mapping operators (rTMOs) enhance low-dynamic-range images and videos for display on high dynamic range monitors. A common problem faced by previous rTMOs is the handling of under or overexposed content. Under such conditions, they may not be effective, and even cause loss and reversal of visible contrast. We present a class of local rTMOs based on cross bilateral filtering that is capable of generating highquality HDR images and videos for a wide range of exposure conditions. Experiments performed using an objective image quality metric show that our approach is the only single technique available that can gracefully enhance perceived details across a large range of image exposures.

Operadores de tone mapping reverso (RTMOs) realçam imagens e vídeos de baixa faixa dinâmica para visualização em monitores de alta faixa dinâmica. Um problema comum encontrado por operadores anteriores é a maneira com que tratam conteúdo sub ou superexposto. Sob tais condições, eles podem não ser eficientes, e até mesmo causar perda e reversão de contraste visível. Apresentamos uma classe de operadores de tone mapping reverso, baseados no filtro bilateral cruzado (cross bilateral filter), capazes de gerar imagens HDR de alta qualidade. Experimentos utilizando uma métrica objetiva de avaliação de imagens demostram que nosso método é a única técnica capaz de realçar detalhes perceptíveis ao longo de uma grande gama de exposições de imagem, a qual inclui desde imagens subexpostas até imagens superexpostas. / Reverse tone mapping operators (rTMOs) enhance low-dynamic-range images and videos for display on high dynamic range monitors. A common problem faced by previous rTMOs is the handling of under or overexposed content. Under such conditions, they may not be effective, and even cause loss and reversal of visible contrast. We present a class of local rTMOs based on cross bilateral filtering that is capable of generating highquality HDR images and videos for a wide range of exposure conditions. Experiments performed using an objective image quality metric show that our approach is the only single technique available that can gracefully enhance perceived details across a large range of image exposures.