Region Adaptive Single Image Dehazing

Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.

PDANet: Self-Supervised Monocular Depth Estimation Using Perceptual and Data Augmentation Consistency

In recent studies, self-supervised learning methods have been explored for monocular depth estimation. They minimize the reconstruction loss of images instead of depth information as a supervised signal. However, existing methods usually assume that the corresponding points in different views should have the same color, which leads to unreliable unsupervised signals and ultimately damages the reconstruction loss during the training. Meanwhile, in the low texture region, it is unable to predict the disparity value of pixels correctly because of the small number of extracted features. To solve the above issues, we propose a network—PDANet—that integrates perceptual consistency and data augmentation consistency, which are more reliable unsupervised signals, into a regular unsupervised depth estimation model. Specifically, we apply a reliable data augmentation mechanism to minimize the loss of the disparity map generated by the original image and the augmented image, respectively, which will enhance the robustness of the image in the prediction of color fluctuation. At the same time, we aggregate the features of different layers extracted by a pre-trained VGG16 network to explore the higher-level perceptual differences between the input image and the generated one. Ablation studies demonstrate the effectiveness of each components, and PDANet shows high-quality depth estimation results on the KITTI benchmark, which optimizes the state-of-the-art method from 0.114 to 0.084, measured by absolute relative error for depth estimation.

Perceptually motivated model for predicting banding artefacts in high-dynamic range images

Banding is a type of quantisation artefact that appears when a low-texture region of an image is coded with insufficient bitdepth. Banding artefacts are well-studied for standard dynamic range (SDR), but are not well-understood for high dynamic range (HDR). To address this issue, we conducted a psychophysical experiment to characterise how well human observers see banding artefacts across a wide range of luminances (0.1 cd/m2–10,000 cd/m2). The stimuli were gradients modulated along three colour directions: black-white, red-green, and yellow-violet. The visibility threshold for banding artefacts was the highest at 0.1 cd/m2, decreased with increasing luminance up to 100 cd/m2, then remained at the same level up to 10,000 cd/m2. We used the results to develop and validate a model of banding artefact detection. The model relies on the contrast sensitivity function (CSF) of the visual system, and hence, predicts the visibility of banding artefacts in a perceptually accurate way.

A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

Static and stability analysis of partial slip texture multi-lobe journal bearings

This paper presents one-dimensional analysis of modified dynamic Reynolds equation derived for partial slip texture multi-lobe journal bearings. The novelty included in this study is the configuration of partial slip texture region on the bottom bearing lobe surface of a multi-lobe journal bearing under a constant vertical load. The nondimensional pressure and shear stress for steady-state and nondimensional pressure gradients for dynamic coefficients for each lobe with partial slip texture configuration are derived based on narrow groove theory. Linearized stability analysis is evaluated using infinitesimal perturbation method. Results of static and stability characteristics of partial slip texture multi-lobe (two-axial groove, elliptical, three-lobe and offset) journal bearings are presented. Partial slip texture configuration significantly enhances load capacity, coefficient of friction, and stability of two-lobe journal bearing.

Image Segmentation Using Contour Models

Image segmentation is an important task in image processing, which is widely used in medical applications such as abnormality detection and after treatment progress monitoring. Conventionally, texture, region, and edge information are used for segmentation. Recently, the majority of image segmentation uses contour-based models. The problem of efficient segmentation in medical images is of great importance in disease diagnosis. Medical images suffer from weak boundaries, and placement of initial contour is a major issue. Level method is an effective method for segmentation of image as it has ability to tackle complex geometries. It helps to detect the precise location of the target region and help to prevent the boundary leakage problem. This chapter presents an overview of the advanced region and edge-based level set segmentation algorithms and their application in the dental x-ray images. Computer-aided diagnosis from x-ray images are of interest to clinicians in detection and accurate decision making. Case studies of multiple region segmentation from dental x-rays are presented.

Image Segmentation Based on Fractional Differentiation and RSF Model

Intensity inhomogeneity or weak texture region image segmentation plays an important role in computer vision and image processing. RSF (Region-Scalable Fitting) active contour model has been proved to be an effective method to segment intensity inhomogeneity. However RSF model is sensitive to the initial location of evolution curve , it tends to fall into local optimal. Aiming at the problem, this paper proposed a new method for image segmentation based on fractional differentiation and RSF model. The proposed method adds the global Grünwald-Letnikov fractional gradient into the RSF model. Thus the gradient of the intensity inhomogeneity and weak texture regions is strengthened. As a result, both the robustness of initial location of evolution curve and efficiency of image segmentation are improved. Theoretical analysis and experimental results demonstrate that the proposed algorithm is capable of segmenting the intensity inhomogeneities and weak texture images. It is robust to curve initial location, furthermore the efficiency of segmentation is improved.