gamma correction
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Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.

This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset.

In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.

Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

Abstract In this paper, an improved block gamma correction algorithm is proposed for the problem of bright light repair in outdoor scenes in the field of artificial intelligence image recognition, and simulation verification is carried out. Firstly, the strong light area is quickly extracted to meet the high-speed and low-calculation requirements of the artificial intelligence image recognition algorithm. Secondly, the strong light area is divided into sub-blocks, and different gamma values are used according to the standard deviation of each sub-block, which effectively reduces the strong light area. Finally, high image detail information and color confidence are preserved, and at the same time, it meets the requirements of the subsequent image recognition network for the input image.

Abstract: Because of underwater pictures application in ocean engineering, ocean research, marine biology, and marine archaeology to name a few, underwater picture enhancement was widely publicized in the last several years. Underwater photos frequently upshot in low contrast, blurred, color distortion, hazy, poor visible images. This is because of light attenuation, absorption, scattering (forward scattering and backward scattering), turbidity, floating particles. As a result, effective underwater picture solution must be developedin order to improve visibility, contrast, and color qualities for greater visual quality and optical attractiveness. Many underwater picture enhancing approaches have been proposed to overcome these challenges; however they all failed to produce accurate results. Hence for this we first undertook a large scale underwater image dataset which is trained by convolution neural network (CNN) and then we have studied and implemented a deep learning approach called very deep super resolution (VDSR) model for improving the color, contrast, and brightness of underwater photos by using different algorithms such as white balance, histogram equalization, and gamma correction respectively. Moreover, our method is compared with the existing method which reveals that our method surpassesthe existing methods Keywords: CNN, gamma correction, histogram equalization, underwater image enhancement, VDSR, white balance

Crossed-grating phase-shifting profilometry (CGPSP) has great utility in three-dimensional shape measurement due to its ability to acquire horizontal and vertical phase maps in a single measurement. However, CGPSP is extremely sensitive to the non-linearity effect of a digital fringe projection system, which is not studied in depth yet. In this paper, a mathematical model is established to analyze the phase error caused by the non-linearity effect. Subsequently, two methods used to eliminate the non-linearity error are discussed in detail. To be specific, a double five-step algorithm based on the mathematical model is proposed to passively suppress the second non-linearity. Furthermore, a precoding gamma correction method based on probability distribution function is introduced to actively attenuate the non-linearity of the captured crossed fringe. The comparison results show that the active gamma correction method requires less fringe patterns and can more effectively reduce the non-linearity error compared with the passive method. Finally, employing CGPSP with gamma correction, a faster and reliable inverse pattern projection is realized with less fringe patterns.