Kavinder Singh

Bio: Kavinder Singh is an academic researcher from Delhi Technological University. The author has contributed to research in topics: Image (mathematics) & Artificial intelligence. The author has an hindex of 5, co-authored 8 publications receiving 54 citations.

A study on Retinex based method for image enhancement

Abstract: In this paper, It focuses on few out of many Retinex based method for Image Enhancement. Retinex is basically a concept of capturing an image in such a way in which a human being perceives it after looking at an object at the place with the help of their retina (Human Eye) and cortex (Mind). On the basis of Retinex theory, we can say an image as a product of illumination and reflectance from the object. Retinex focuses on dynamic range and color constancy of an image. There are various methods proposed by various researchers till date which use Retinex for image contrast enhancement. In this paper, we will discuss Single Scale Retinex (SSR), Multi-Scale Retinex (MSR), Improved Retinex Image Enhancement (IRIE), MSR improvement for night time Enhancement (MSRINTE) and Retinex Based Perceptual Contrast Enhancement in image using luminance adaptation (RBPCELA).

A Comparative Study of Image Dehazing Algorithms

Abstract: Haze poses a great challenge in modern-day applications. Removing haze is a challenging task because haze varies with the depth of the scenes in the image. Many automated systems like surveillance systems, object tracking, etc. use the dehazing methods internally to improve their overall performance in a hazy environment. Hence, recovering the original image from the hazy image is a crucial task. This paper provides a detailed survey of many algorithms that have been proposed for haze removal, which refines the image, either by using statistical observation of the scene or by network-based learning method. These algorithms have been assessed quantitatively and visually to compare based on their dehazing potential. The paper presents a comparative study of the various image dehazing methods having a different way of estimation of transmission and atmospheric light. This paper reviews different prior-based technique and learning-based technique image dehazing algorithms.

A comparative analysis of illumination estimation based Image Enhancement techniques

Abstract: Computer vision has gathered the attention of several researchers due to the incremental use of it in almost every field. The performance of computer vision systems is many times limited by input image quality. Thus, image enhancement becomes an essential step in these systems. Retinex based algorithms have proven performance in enhancement of low light images. Many Retinex-based algorithms focus on illumination estimation to perform image enhancement. This paper presents a comparative analysis of state-of-the-art image enhancement algorithms based on illumination estimation. In Retinex based approaches, computation of illumination and reflectance is a challenging task. In early approaches, a smooth image is considered as the illumination. However, in the last decade, various methods for estimation of illumination and reflectance have evolved up to a great extent. In this work, we analyze these image enhancement techniques based on illumination estimation. We perform extensive experimentation on a large set of images with varying illumination. The performance is analyzed both quantitatively and qualitatively. This analysis aims to assist researchers and ignite research to develop new efficient algorithms in this field.

Getting to Know Low-light Images with The Exclusively Dark Dataset

Abstract: Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light has seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark dataset such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i.e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing visualizations of both hand-crafted and learned features. Most importantly, we found that the effects of low-light reaches far deeper into the features than can be solved by simple "illumination invariance'". It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The Exclusively Dark dataset with its annotation is available at this https URL

Exposure Based Multi-Histogram Equalization Contrast Enhancement for Non-Uniform Illumination Images

Abstract: Non-uniform illuminated images pose challenges in contrast enhancement due to the existence of different exposure region caused by uneven illumination. Although Histogram Equalization (HE) is a well-known method for contrast improvement, however, the existing HE-based enhancement methods for non-illumination often generated the unnatural images, introduced unwanted artifacts, and washed out effect because they do not utilize the information from the different exposure regions in performing equalization. Therefore, this study proposes a modified HE-based contrast enhancement technique for non-uniform illuminated images namely Exposure Region-Based Multi-Histogram Equalization (ERMHE). The ERMHE uses exposure region-based histogram segmentation thresholds to segment the original histogram into sub-histograms. With the thresholded sub-histograms, the ERMHE then uses an entropy-controlled gray level allocation scheme to allocate new output gray level range and to obtain new thresholds that will be used to repartition the histogram prior to HE process. A total of 154 non-uniform illuminated sample images are used to evaluate the application of the proposed ERMHE. By comparing ERMHE to four existing HE-based contrast enhancement namely, Global HE, Mean Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE), qualitatively, the ERMHE produces enhanced images with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. Quantitatively, the ERMHE achieves the highest peak signal-to-noise-ratio (PSNR), lowest Absolute Mean Brightness Error (AMBE), and second best in Discrete Entropy (DE) scores. From the analyses, the ERMHE has shown its capability in enhancing different exposure regions exist in non-uniform illuminated images.

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

Abstract: In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

A Comparative Study of Image Dehazing Algorithms

Abstract: Haze poses a great challenge in modern-day applications. Removing haze is a challenging task because haze varies with the depth of the scenes in the image. Many automated systems like surveillance systems, object tracking, etc. use the dehazing methods internally to improve their overall performance in a hazy environment. Hence, recovering the original image from the hazy image is a crucial task. This paper provides a detailed survey of many algorithms that have been proposed for haze removal, which refines the image, either by using statistical observation of the scene or by network-based learning method. These algorithms have been assessed quantitatively and visually to compare based on their dehazing potential. The paper presents a comparative study of the various image dehazing methods having a different way of estimation of transmission and atmospheric light. This paper reviews different prior-based technique and learning-based technique image dehazing algorithms.