scispace - formally typeset
Journal ArticleDOI

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Reads0
Chats0
TLDR
Experimental results demonstrate that the proposed enhancement algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.
Abstract
Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

read more

Citations
More filters
Journal ArticleDOI

An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

TL;DR: Wang et al. as mentioned in this paper reviewed the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions, in terms of the underwater image formation model (IFM).
Journal ArticleDOI

Beyond Brightening Low-light Images

TL;DR: Zhang et al. as discussed by the authors proposed a divide-and-conquer network, which decomposes images into two components: one component is responsible for light adjustment, while the other component is used for degradation removal.
Journal ArticleDOI

LECARM: Low-Light Image Enhancement Using the Camera Response Model

TL;DR: This work proposes a novel enhancement framework using the response characteristics of cameras to lower the distortions, and can obtain enhancement results with fewer color and lightness distortions compared with the several state-of-the-art methods.
Posted Content

Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset

TL;DR: This paper proposes a novel end-to-end attention-guided method based on multi-branch convolutional neural network that can produce high fidelity enhancement results for low-light images and outperforms the current state-of-the-art methods both quantitatively and visually.
Journal ArticleDOI

Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement

TL;DR: Wang et al. as mentioned in this paper designed an end-to-end signal prior-guided layer separation and data-driven mapping network with layer-specified constraints for single-image low-light enhancement.
References
More filters
Journal ArticleDOI

Lightness and Retinex Theory

TL;DR: The mathematics of a lightness scheme that generates lightness numbers, the biologic correlate of reflectance, independent of the flux from objects is described.
Journal ArticleDOI

A multiscale retinex for bridging the gap between color images and the human observation of scenes

TL;DR: This paper extends a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition and defines a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency.
Book

Handbook of Image and Video Processing

Alan C. Bovik
TL;DR: The Handbook of Image and Video Processing contains a comprehensive and highly accessible presentation of all essential mathematics, techniques, and algorithms for every type of image and video processing used by scientists and engineers.
Journal ArticleDOI

Properties and performance of a center/surround retinex

TL;DR: A practical implementation of the retinex is defined without particular concern for its validity as a model for human lightness and color perception, and the trade-off between rendition and dynamic range compression that is governed by the surround space constant is described.
Journal ArticleDOI

Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement

TL;DR: A system level realization of CLAHE is proposed, which is suitable for VLSI or FPGA implementation and the goal for this realization is to minimize the latency without sacrificing precision.
Related Papers (5)