Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

IJOSTHE ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Input Image ◽  
Sigmoid Function ◽  
Single Image ◽  
Low Light ◽  
Lighting Condition ◽  
Low Contrast ◽  
General Terms ◽  
Degraded Image ◽  
Result Analysis

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.

scholarly journals Enhancing Multi Exposure Images Using Convolution Neural Network

2019 ◽  
pp. 223-228
Author(s):  
Sunitha Nandhini A ◽  
Anjani A L ◽  
Indhuja R ◽  
Jeevitha D
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Input Image ◽  
Reference Image ◽  
Single Image ◽  
Data Set ◽  
Lighting Condition ◽  
Low Contrast ◽  
Information Set ◽  
Image Pairs

Due to the poor lighting condition and restricted dynamic vary of digital imaging devices, the recorded photos are usually under-/over-exposed and with low distinction. Most of the previous single image distinction improvement (SICE) strategies modify the tone curve to correct the distinction of an associated input image. Those strategies, however, typically fail in revealing image details due to the restricted data in a very single image. On the opposite hand, the SICE task is often higher accomplished if we will learn additional info from suitably collected coaching information. In this paper, we have a tendency to propose to use the convolutional neural network (CNN) to coach SICE attention. One key issue is the way to construct a coaching information set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this finish, we have a tendency to build a large-scale multi-exposure image knowledge set, that contains 589 in an elaborate way chosen high-resolution multi-exposure sequences with four, 413 images. Thirteen representatives multi-exposure image fusion and stack-based high dynamic vary imaging algorithms are accustomed urge the excellence enhanced footage for each sequence, and subjective experiments are conducted to screen the best quality one because of the reference image of every 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 benefits of our methodology over existing SICE strategies with a major margin.

scholarly journals Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

2020 ◽  
Vol 37 (5) ◽  
pp. 733-743
Author(s):  
Mohammad Abid Al-Hashim ◽  
Zohair Al-Ameen
Keyword(s):  
Image Enhancement ◽  
Processing Speed ◽  
Sigmoid Function ◽  
Light Effect ◽  
Low Light ◽  
Single Scale ◽  
Retinex Algorithm ◽  
Adequate Quality ◽  
Normalization Function

These days, digital images are one of the most profound methods used to represent information. Still, various images are obtained with a low-light effect due to numerous unavoidable reasons. It may be problematic for humans and computer-related applications to perceive and extract valuable information from such images properly. Hence, the observed quality of low-light images should be ameliorated for improved analysis, understanding, and interpretation. Currently, the enhancement of low-light images is a challenging task since various factors, including brightness, contrast, and colors should be considered effectively to produce results with adequate quality. Therefore, a retinex-based multiphase algorithm is developed in this study, in that it computes the illumination image somewhat similar to the single-scale retinex algorithm, takes the logs of both the original and the illumination images, subtract them using a modified approach, the result is then processed by a gamma-corrected sigmoid function and further processed by a normalization function to produce to the final result. The proposed algorithm is tested using natural low-light images, evaluated using specialized metrics, and compared with eight different sophisticated methods. The attained experiential outcomes revealed that the proposed algorithm has delivered the best performances concerning processing speed, perceived quality, and evaluation metrics.

scholarly journals Tag Disentangled Generative Adversarial Network for Object Image Re-rendering

2017 ◽  
Author(s):  
Chaoyue Wang ◽  
Chaohui Wang ◽  
Chang Xu ◽  
Dacheng Tao
Keyword(s):  
Input Image ◽  
Generative Adversarial Networks ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Adversarial Training ◽  
Realistic Images

In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversarial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.

scholarly journals Generation of Stereo Images Based on a View Synthesis Network

2020 ◽  
Vol 10 (9) ◽  
pp. 3101
Author(s):  
Yuan-Mau Lo ◽  
Chin-Chen Chang ◽  
Der-Lor Way ◽  
Zen-Chung Shih
Keyword(s):  
Input Image ◽  
View Synthesis ◽  
Reference Image ◽  
Single Image ◽  
Stereo Images ◽  
Flow Network ◽  
Better Than

The conventional warping method only considers translations of pixels to generate stereo images. In this paper, we propose a model that can generate stereo images from a single image, considering both translation as well as rotation of objects in the image. We modified the appearance flow network to make it more general and suitable for our model. We also used a reference image to improve the inpainting method. The quality of images resulting from our model is better than that of images generated using conventional warping. Our model also better retained the structure of objects in the input image. In addition, our model does not limit the size of the input image. Most importantly, because our model considers the rotation of objects, the resulting images appear more stereoscopic when viewed with a device.

scholarly journals Lab Image and Field Image Contrast Quality Differentiation

2020 ◽  
Vol 9 (3) ◽  
pp. 3428-3431
Keyword(s):  
Image Quality ◽  
System Performance ◽  
Input Image ◽  
Image Size ◽  
Standard Size ◽  
Low Contrast ◽  
Quality Differentiation ◽  
Image Dimension ◽  
Processing Material

Image processing performance can be improved with the process of resizing the original input image to one standard size. Most of the previous studies used a standard size of 256 x 256 to provide the image as the image pre-processing material. The result of different image size dimension are shows in this research to proven that image resizing is important. Reducing image dimension size can help to improve system performance. At the same time, it is importance to keep the image quality. This study shows that by reducing image dimension, it can improve the computer or system performance more than 95%. Image quality can be measured to get helpful information for the study after resizing the image into the same standard size. In this study, measurement of contrast levels was taken to compare the quality differences between image labs and field images. It turns out that the quality of lab image produces high-quality images with good brightness over image field image.The best quality image is the images that have low contrast. Therefore in this research paper we used CLAHE method to enhance the contrast and brightness for field image.

scholarly journals A SEC Vidicon System for Satellite Applications

1971 ◽  
Vol 41 ◽  
pp. 360-360
Author(s):  
G. E. Brückner
Keyword(s):  
Signal To Noise Ratio ◽  
Light Level ◽  
Zero Order ◽  
Signal To Noise ◽  
Compression Scheme ◽  
Low Light ◽  
Low Contrast ◽  
Satellite Applications ◽  
Theoretical Considerations

This paper describes a small, low power SEC vidicon camera developed for recording images of the outer solar corona from an OSO satellite. The SEC vidicon has been selected for this application because of its capability to integrate and store low light level and low contrast images. The achieved signal to noise ratio will be discussed and compared with theoretical considerations. A new operational method to readout the tube very slowly will be described. The influence of a zero order data compression scheme on the quality of the coronal images will be discussed.

scholarly journals Low-Light Image Enhancement Based on Multi-Path Interaction

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4986
Author(s):  
Bai Zhao ◽  
Xiaolin Gong ◽  
Jian Wang ◽  
Lingchao Zhao
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Interaction Network ◽  
Feature Representation ◽  
Uniform Illumination ◽  
Feature Maps ◽  
Low Light ◽  
Low Contrast ◽  
Better Than

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding–decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.

Resistance Monitor and Control for Vacuum Evaporation of Metals and Carbon

1976 ◽  
Vol 34 ◽  
pp. 572-573
Author(s):  
Russell L. Steere ◽  
Eric F. Erbe ◽  
J. Michael Moseley
Keyword(s):  
Electronic Device ◽  
Point Sources ◽  
High Contrast ◽  
Variable Resistor ◽  
Quality Of Results ◽  
Low Contrast ◽  
Evaporation Source ◽  
And Control ◽  
At Will

We have designed and built an electronic device which compares the resistance of a defined area of vacuum evaporated material with a variable resistor. When the two resistances are matched, the device automatically disconnects the primary side of the substrate transformer and stops further evaporation.This approach to controlled evaporation in conjunction with the modified guns and evaporation source permits reliably reproducible multiple Pt shadow films from a single Pt wrapped carbon point source. The reproducibility from consecutive C point sources is also reliable. Furthermore, the device we have developed permits us to select a predetermined resistance so that low contrast high-resolution shadows, heavy high contrast shadows, or any grade in between can be selected at will. The reproducibility and quality of results are demonstrated in Figures 1-4 which represent evaporations at various settings of the variable resistor.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

Eating Out in the Nineteenth and Twentieth Centuries: Changes in Food and in Social Identities

2018 ◽  
Author(s):  
Christel Lane
Keyword(s):  
Twentieth Century ◽  
Social Identities ◽  
Rapid Urbanization ◽  
General Terms ◽  
Quality Of Food ◽  
Nineteenth And Twentieth Centuries ◽  
Eating Out ◽  
Gender And Nation ◽  
The Impact

This chapter examines the impact of rapid urbanization and industrialization on food and eating out. It draws attention to the growing standardization of food and, with greater class differentiation, to the growing diversity in eating-out venues. Class, gender, and nation are again used as lenses to understand the different eating-out habits and their symbolic significance. Towards the end of the twentieth century, pubs moved more fully towards embracing dining. However, the quality of food, in general terms, began to improve significantly only towards the end of the century, and hospitality venues also moved towards selling food from diverse national origins.

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