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Most existing video content today are in Standard Dynamic Range (SDR) format and there is a growing necessity to upscale this content for High Dynamic Range (HDR) displays, our goal is to develop a... Continue reading...

Since the light will be absorbed and scattered when travels in water, underwater imaging exists three major difficulties, including color cast, under-exposure, and fuzz. The solutions to overcome those issues are important for the exploration of the ocean. In this paper, we propose a new algorithm for improving the quality of underwater images. The algorithm is composed of two components: color correction and illumination adjustment. First, we use an efficient color enhancement method to solve the color cast. Then, based on Retinex model, we make the illumination adjustment, mainly extracting the illumination map and implementing gamma correction on it successively. Experimental results show that visual performance of our method outperforms that of other methods, and processing complexity is relatively simpler. Continue reading...

Low-light image enhancement algorithms can improve the subjective visual quality of low-light images and support the extraction of valuable information for some computer vision techniques. Although many low-light image enhancement algorithms have been developed in recent years, the assessment methods for enhanced images are still open topics. In this paper, we use multi-exposure image sequences and a camera response model to calculate the color distortion of enhanced images. Specifically, we first select the useful information from a multi-exposure image sequence to form a reference image based on the illumination of the enhanced image. Then, we calculate the exposure ratio map between the reference image and the enhanced image for each color channel using the camera response model. Finally, the color distortion is calculated based on the difference between three color channel exposure ratios. Experiment results show that the Pearson’s linear correction coefficient (PLCC) and Spearman’s rank correlation coefficient (SRCC) between the results of our reduced-reference method and that of the full-reference CIE Lab color difference method (∆Eab ) are close to 1. Meanwhile, our method is significantly superior to the existing enhanced image color metrics. Continue reading...

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. To solve this problem, many image enhancement techniques have been proposed. However, existing techniques inevitably introduce color and lightness distortion when increasing visibility. To lower the distortion, we propose a novel enhancement method using the response characteristics of cameras. First, we investigate the relationship between two images with different exposures to obtain an accurate camera response model. Then we borrow the illumination estimation techniques to estimate the exposure ratio map. Finally, we use our camera response model to adjust each pixel to its desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with less color and lightness distortion compared to several state-of-the-art methods. Continue reading...

In this paper, we present a novel method for single-image shadow removal. From the observation of images with shadow, we find that the pixels from the object with same material will form a line in the RGB color space as illumination changes. Besides, we find these lines do not cross with the origin due to the effect of ambient light. Thus, we establish an offset correction relationship to remove the effect of ambient light. Then we derive a linear shadow image model to perform color-line identification. With the linear model, our shadow removal method is proposed as following. First, perform color-line clustering and illumination estimation. Second, use an on-the-fly learning method to detect umbra and penumbra. Third, estimate the shadow scale by the statistics of shadow-free regions. Finally, refine the shadow scale by illumination optimization. Our method is simple and effective for producing high-quality shadow-free images and has the ability for processing scenes with rich texture types and non-uniform shadows. Continue reading...

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. In this paper, we propose an exposure fusion framework and an enhancement algorithm to provide an accurate contrast enhancement. Continue reading...

还是先画关系图 解法一：局部到全体 解法二：反向递推 神经元间到神经元内：由于网络结构以神经元为单位，以 z 代表一个神经元求，先省略 w,b 神经元间解法一：局部到全体 (分子布局)先求解各个边，再找路径，路径上相乘，路径间相加。 输出层到损失函数$$\frac{ \partial C}{\partial z_i^{(L)}} = \frac{ \partial C}{\partial... Continue reading...

Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second, we present a simple and effective way to detect and remove the offset. The resulting images, named ORGB, have almost the same appearance as the original RGB images while are more illumination-robust for color space conversion. Besides, image processing using ORGB instead of RGB is free from the interference of shadows. Finally, the proposed offset correction method is ap- plied to road detection task, improving the performance both in quantitative and qualitative evaluations. Continue reading...

In this paper, we address the problem of searching action proposals in unconstrained video clips. Our approach starts from actionness estimation on frame-level bounding boxes, and then aggregates the bounding boxes belonging to the same actor across frames via linking, associating, tracking to generate spatial-temporal continuous action paths. To achieve the target, a novel actionness estimation method is firstly proposed by utilizing both human appearance and motion cues. Then, the association of the action paths is formulated as a maximum set coverage problem with the results of actionness estimation as a priori as in [1]. To further promote the performance, we design an improved optimization objective for the problem and provide a greedy search algorithm to solve it. Finally, a tracking-by-detection scheme is designed to further refine the searched action paths. Extensive experiments on two challenging datasets, UCF-Sports and UCF-101, show that the proposed approach advances state-of-the-art proposal generation performance in terms of both accuracy and proposal quantity. Continue reading...

Road detection is one of the most important research areas in driver assistance and automated driving field. However, the performance of existing methods is still unsatisfactory, especially in severe shadow conditions. To overcome those difficulties, first we propose a novel shadow-free feature extractor based on the color distribution of road surface pixels. Then we present a road detection framework based on the extractor, whose performance is more accurate and robust than that of existing extractors. Also, the proposed framework has much low-complexity, which is suitable for usage in practical systems. Continue reading...