In this paper, we present a block-matching and 3-D filtering (BM3D) algorithm dedicated to the division-of-focal-plane (DoFP) polarization images. This algorithm based on a non-local collaborative filtering method is capable of exploiting all the different polarization channels simultaneously. Compared with the previously reported implementations for DoFP sensors, the proposed algorithm attenuates Gaussian noise in the transform domain by stacking similar 2-D image patches to form a 3-D block. According to our extensive experimental results, the proposed algorithm outperforms all the existing denoising algorithms for DoFP images including the state-of-the-art principle component analysis in terms of peak-signal-to-noise-ratio and structural similarity index. Moreover, the comparison is further extended to visual comparison, it is indicated that the image details are well-preserved by the proposed BM3D algorithm.

In this paper, we present a hybrid denoising algorithm dedicated to division-of-focal plane (DoFP) polarization images. The proposed algorithm, centered around the Block-Matching and 3D Filtering (BM3D) and K-times Singular Value Decomposition (KSVD) denoising algorithms, is capable of significantly enhancing the grouping step in the second round of collaborative filtering by purifying the “Semi-Filtered” image yielded by the first round of collaborative filtering. To achieve this, the BM3D denoising method’s chain of operation is broken, and the “Semi-Filtered” image is passed through a round of KSVD denoising method before the second round of collaborative filtering is conducted. According to our extensive experimental results, the proposed algorithm visually outperforms the state-of-the-art BM3D denoising algorithm and a wide range of other denoising algorithms for DoFP polarization images. Quantitative results presented using Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) Index metrics further highlight the superior performance of the proposed algorithm.

In this paper, we present a novel K times singular value decomposition (K-SVD) based denoising algorithm dedicated to the division-of-focal-plane (DoFP) polarization image sensors. The proposed method is based on sparse representation over trained dictionary. Using the proposed K-SVD algorithm to update the dictionary, the image content can be more effectively expressed. Compared with the previous denoising algorithms, the proposed implementation is capable of decomposing the input DoFP image as the optimum sparse combination of the dictionary elements, which are generated by orthogonal matching pursuit (OMP). This not only separates the Gaussian noise from the target DoFP image with a significantly elevated peak signal-tonoise ratio (PSNR), but also well-preserves the details of the original image. According to our extensive experimental results on various test images, the proposed algorithm outperforms the state-of-the-art principal component analysis (PCA) based denoising algorithm by 3dB in term of PSNR. Moreover, visual comparison results, which show excellent agreement with the PSNR results, are presented as well.

Good nutrition is an important part of leading a healthy lifestyle. This has brought into stark focus the need for efficient and low-cost methods for large scale food quality assessment. This paper proposes a non-invasive and non-destructive system for estimating the freshness of apples using polarization images from a Division-of-Focal-Plane (DoFP) polarization camera. The proposed system uses Machine Learning Systems namely, Support Vector Regression (SVR) and Gaussian Process Regression (GPR), to estimate the age of apples and determine if they are fit for consumption even before the external rot appears on the fruit. Initially, the reconstructed images namely, Degree of Linear Polarization (DoLP) and Angle of Polarization (AoP), are generated from the polarization image and their respective correlations with the actual age of apples (in days) are established. These reconstructed images are then fed as input features to the Machine Learning Systems to ultimately estimate the age of the apples. Experiments on real data obtained from the DoFP camera show that the proposed system is non-destructive and capable of non-invasively estimating the age of the apple with an average accuracy of up to 92.57%.

This paper studies the problem of automated noninvasive skin cancer (melanoma) detection from digital images of skin lesions. It proposes the use of Bayesian Decision Fusion of a multiple of classifiers to enhance the melanoma detection rates. A comparison with other decision fusion systems along with standalone classifiers in terms of accuracy and confidence intervals is made. The relation between confidence distribution and accuracy over different classification systems is studied. Performance evaluations of the proposed Bayesian Decision fusion method shows that it results in improved recognition accuracy compared to standalone Skin Lesion classifiers. It also provides comparable confidence intervals and can offer stable recognition rate. Hence, it can lead to increased chances of non-invasive melanoma detection.

In this paper, we propose a machine learning system for the estimation of atmospheric particulate matter (PM) concentration, specifically, particles with a maximum diameter of 2.5μ m. These very fine particles, also known as PM2:5 particles, are very dangerous to the human body as they are small enough to penetrate deep areas of the vital organs. The proposed system uses a combination of features from both polarimetric and spectral imaging modalities in training and developing a machine learning model that provides high accuracy PM2.5 estimates. Furthermore, acquisition of the polarimetric images is done near the ground surface with a horizontal field of view aiming at standard targets which enables higher accuracy at the surface level. The accuracy of the approach was verified through a study conducted during the summer months of the United Arab Emirates (UAE). The proposed system employs different machine learning techniques such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Bagging Ensemble Trees (BET), to provide high accuracy PM2.5 estimates. Our proposed system achieves the best performance within the red wavelength with accuracy up to 93.8627% and an R2 score up to 0.9420.