Yücel Çimtay

Many dehazing methods have been presented by many researchers either on single hazy images or multiple images like videos. Single image dehazing is a very challenging problem, since there is no ground truth image and/or depth map. Beyond many traditional dehazing methods, with the increasing volume of hazy image datasets, recent literature about dehazing mostly focuses on deep learning methods. Although, deep learning models perform better than traditional methods on the validation data of the specific dataset which they are trained with, the generalization performance of deep learning models is generally poor. Another important bottleneck for deep models is the training cost in terms of time and hardware requirements. In this study, Atmospheric Light Scattering model is improved by taking into account the amount of haze and refining the transmission and air light used in atmospheric light scattering model. According to VIS quality metric results, proposed method is the second best method on Dense-Haze and CHIC-Color datasets by achieving 0.87 and 0.86 scores, respectively, and the best method on our own IMFD dataset by handling 0.91. Results of this study shows that proposed method is superior and/or competitive to the state of the art deep learning models in terms of the visual quality of the dehazed image.

Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become invisible or scarcely visible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering (ALS) Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods, which solve the ALS function, there are basically two steps: First one is the estimation of the air light present at the time of the image capturing and the second one is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition (HD) imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real-time or near real-time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then is used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0% and 65% for image resolutions of (640x480) and (1920x1080) compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.

Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicators developed in the literature is the Normalized Difference Nitrogen Index (NDNI), which is based on the usage of the spectral bands of 1510 and 1680 nm from the Short-Wave Infrared (SWIR) region of the electromagnetic spectrum. However, the majority of remote sensing sensors, like cameras and/or satellites, do not have an SWIR sensor due to high costs. Many vegetation indexes, like NDVI, EVI and MNLI, have also been developed in the VNIR region to monitor the greenness and health of the crops. However, these indexes are not very well correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as the input and NDNI as the output, both of which are calculated from the same location. After training, an overall correlation of 0.93 was achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy.

Many dehazing methods have been presented by many researchers either on single hazy images or multiple images like videos. With the increasing volume of hazy image datasets, recent literature about dehazing is mostly based on deep learning methods. Although, deep learning models perform better than traditional methods on the validation data of the specific dataset which they are trained with, the generalization performance of deep learning models is generally poor. Another important bottleneck for deep models is the training cost in terms of time and hardware requirements. In this study, traditional DCP method is improved by taking into account the amount of haze and refining the transmission and air light used in atmospheric light scattering model of haze. Results of this study shows that proposed method is superior and/or competitive to traditional DCP method and state of the art deep learning models in terms of the visual quality of the dehazed image.

Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicator developed in the literature is Normalized Difference Nitrogen Index (NDNI) which is based on the usage of the spectral bands: 1510 and 1680 nm. from Short-Wave Infrared (SWIR) region of electromagnetic spectrum. However, majority of the remote sensing sensors like cameras and/or satellites do not have a SWIR sensor due to the high costs. Many vegetation indexes like NDVI, EVI, MNLI, have been developed in also VNIR region to monitor the greenness and healthy of the crops. However these indexes are not very correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as input and NDNI as output which are both calculated from the same location. After training, 0.93 correlation is achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy.

Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become scarcely visible or invisible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering (ALS) Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods which solve the ALS function, there are basically two steps. First one is the estimation of the air light present at the time of the image capturing and the second step is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition (HD) imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real time or near real time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then can be used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0% and 65% for image resolutions of (640x480) and (1920x1080) compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.