Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

When one captures images in low-light conditions, the images often suffer from low visibility. Besides degrading the visual aesthetics of images, this poor quality may also significantly degenerate the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a simple yet effective low-light image enhancement (LIME) method. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G, and B channels. Furthermore, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.

https://ieeexplore.ieee.org/ielaam/83/7763897/7782813-aam.pdf

LIME: Low-Light Image Enhancement via Illumination Map Estimation

Infrared and visible image fusion methods and applications: A survey

We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.

/pdf/a-weighted-variational-model-for-simultaneous-reflectance-nlowrs8cm1.pdf

A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. Most of previous single image contrast enhancement (SICE) methods adjust the tone curve to correct the contrast of an input image. Those methods, however, often fail in revealing image details because of the limited information in a single image. On the other hand, the SICE task can be better accomplished if we can learn extra information from appropriately collected training data. In this paper, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this end, we build a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images. Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the constructed data set, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the advantages of our method over existing SICE methods with a significant margin.

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Illumination estimation is important for image enhancement based on Retinex. However since illumination estimation is an ill-posed problem it is difficult to achieve accurate illumination estimation for nonuniform illumination images. The conventional illumination estimation algorithms fail to comprehensively take all the constraints into the consideration such as spatial smoothness sharp edges on illumination boundaries and limited range of illumination. Thus these algorithms cannot effectively and efficiently estimate illumination while preserving naturalness. In this paper we present a naturalness preserved illumination estimation algorithm based on the proposed joint edge-preserving filter which exploits all the abovementioned constraints. Moreover a fast estimation is implemented based on the box filter. Experimental results demonstrate that the proposed algorithm can achieve the adaptive smoothness of illumination beyond edges and ensure the range of the estimated illumination. When compared with other state-of-the-art algorithms it can achieve better quality from both subjective and objective aspects.

Naturalness Preserved Nonuniform Illumination Estimation for Image Enhancement Based on Retinex

Visible videos captured under different weather conditions may exhibit different characteristics, and thermal infrared videos are easily affected by ambient temperature variations; this sensitivity to environmental conditions makes the fusion of visible and thermal infrared videos a challenge. This paper proposes an adaptive fusion algorithm for visible and infrared videos, and uses cumulative distribution of gray levels and the entropy to adaptively retain infrared-hot targets and visible textures. The original visible and infrared frames are decomposed into two layers, namely, the base layer and the detail layer. The guided filter is employed to decompose frames due to its high efficiency. Two weight maps, one for the infrared base layer and one for the visible base layer, are adaptively generated based on the cumulative distribution of gray levels and the entropy, respectively. The visible base layer and the infrared base layer are fused based on their weight maps. The final fusion result is obtained by combining the fused base layer with the visible detail layer. Experimental results demonstrate that the proposed algorithm can achieve better fusion results compared with state-of-the-art methods.

An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera. Motivated by the observation that pedestrians of disparate spatial scales exhibit distinct visual appearances, we propose in this paper an active pedestrian detector that explicitly operates over multiple-layer neuronal representations of the input still image. More specifically, convolutional neural nets, such as ResNet and faster R-CNNs, are exploited to provide a rich and discriminative hierarchy of feature representations, as well as initial pedestrian proposals. Here each pedestrian observation of distinct size could be best characterized in terms of the ResNet feature representation at a certain layer of the hierarchy. Meanwhile, initial pedestrian proposals are attained by the faster R-CNNs techniques, i.e., region proposal network and follow-up region of interesting pooling layer employed right after the specific ResNet convolutional layer of interest, to produce joint predictions on the bounding-box proposals' locations and categories (i.e., pedestrian or not). This is engaged as an input to our active detector, where for each initial pedestrian proposal, a sequence of coordinate transformation actions is carried out to determine its proper x-y 2D location and the layer of feature representation, or eventually terminated as being background. Empirically our approach is demonstrated to produce overall lower detection errors on widely used benchmarks, and it works particularly well with far-scale pedestrians. For example, compared with 60.51% log-average miss rate of the state-of-the-art MS-CNN for far-scale pedestrians (those below 80 pixels in bounding-box height) of the Caltech benchmark, the miss rate of our approach is 41.85%, with a notable reduction of 18.66%.

Hai-Miao Hu

Papers

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Naturalness Preserved Nonuniform Illumination Estimation for Image Enhancement Based on Retinex

An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy

A fast image dehazing algorithm based on negative correction