1,139 research outputs found
Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling
In this paper, we propose a general framework to accelerate the universal
histogram-based image contrast enhancement (CE) algorithms. Both spatial and
gray-level selective down- sampling of digital images are adopted to decrease
computational cost, while the visual quality of enhanced images is still
preserved and without apparent degradation. Mapping function calibration is
novelly proposed to reconstruct the pixel mapping on the gray levels missed by
downsampling. As two case studies, accelerations of histogram equalization (HE)
and the state-of-the-art global CE algorithm, i.e., spatial mutual information
and PageRank (SMIRANK), are presented detailedly. Both quantitative and
qualitative assessment results have verified the effectiveness of our proposed
CE acceleration framework. In typical tests, computational efficiencies of HE
and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.Comment: accepted by IET Image Processin
Two Decades of Colorization and Decolorization for Images and Videos
Colorization is a computer-aided process, which aims to give color to a gray
image or video. It can be used to enhance black-and-white images, including
black-and-white photos, old-fashioned films, and scientific imaging results. On
the contrary, decolorization is to convert a color image or video into a
grayscale one. A grayscale image or video refers to an image or video with only
brightness information without color information. It is the basis of some
downstream image processing applications such as pattern recognition, image
segmentation, and image enhancement. Different from image decolorization, video
decolorization should not only consider the image contrast preservation in each
video frame, but also respect the temporal and spatial consistency between
video frames. Researchers were devoted to develop decolorization methods by
balancing spatial-temporal consistency and algorithm efficiency. With the
prevalance of the digital cameras and mobile phones, image and video
colorization and decolorization have been paid more and more attention by
researchers. This paper gives an overview of the progress of image and video
colorization and decolorization methods in the last two decades.Comment: 12 pages, 19 figure
Deep visible and thermal image fusion for enhanced pedestrian visibility
Reliable vision in challenging illumination conditions is one of the crucial requirements of future autonomous automotive systems. In the last decade, thermal cameras have become more easily accessible to a larger number of researchers. This has resulted in numerous studies which confirmed the benefits of the thermal cameras in limited visibility conditions. In this paper, we propose a learning-based method for visible and thermal image fusion that focuses on generating fused images with high visual similarity to regular truecolor (red-green-blue or RGB) images, while introducing new informative details in pedestrian regions. The goal is to create natural, intuitive images that would be more informative than a regular RGB camera to a human driver in challenging visibility conditions. The main novelty of this paper is the idea to rely on two types of objective functions for optimization: a similarity metric between the RGB input and the fused output to achieve natural image appearance; and an auxiliary pedestrian detection error to help defining relevant features of the human appearance and blending them into the output. We train a convolutional neural network using image samples from variable conditions (day and night) so that the network learns the appearance of humans in the different modalities and creates more robust results applicable in realistic situations. Our experiments show that the visibility of pedestrians is noticeably improved especially in dark regions and at night. Compared to existing methods we can better learn context and define fusion rules that focus on the pedestrian appearance, while that is not guaranteed with methods that focus on low-level image quality metrics
Põhjalik uuring ülisuure dünaamilise ulatusega piltide toonivastendamisest koos subjektiivsete testidega
A high dynamic range (HDR) image has a very wide range of luminance levels that
traditional low dynamic range (LDR) displays cannot visualize. For this reason, HDR
images are usually transformed to 8-bit representations, so that the alpha channel for
each pixel is used as an exponent value, sometimes referred to as exponential notation
[43]. Tone mapping operators (TMOs) are used to transform high dynamic range to
low dynamic range domain by compressing pixels so that traditional LDR display can
visualize them. The purpose of this thesis is to identify and analyse differences and
similarities between the wide range of tone mapping operators that are available in the
literature. Each TMO has been analyzed using subjective studies considering different
conditions, which include environment, luminance, and colour. Also, several inverse
tone mapping operators, HDR mappings with exposure fusion, histogram adjustment,
and retinex have been analysed in this study. 19 different TMOs have been examined
using a variety of HDR images. Mean opinion score (MOS) is calculated on those selected
TMOs by asking the opinion of 25 independent people considering candidates’
age, vision, and colour blindness
- …