Low Illumination Image Enhancement Algorithm Using Iterative Recursive Filter and Visual Gamma Transformation Function

David, D. Beulah

doi:10.1109/icacc.2015.43

Cited by 6 publications

(2 citation statements)

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“…Moreover, an arctangent hyperbola has been used to map the hue component of an image to an appropriate range by Yu et al [61], and later, low-light image enhancement based on the optimal hyperbolic tangent profile was proposed [62]. Nonlinear transformation requires more complex calculations and consequently a longer time than linear transformation [63], [64].…”

Section: (B)mentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: (B)mentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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“…Singh et al 11 performed image enhancement by applying power law transformation on global discrete domain transform of an input image and achieved better EME. Gamma transformation and iterative recursive filter 12 are used together for low illumination image enhancement. A biologically inspired retinex-based algorithm 13 is used to enhance the under or overexposed regions of standard dynamic range images.…”

Section: Image Enhancement Techniquesmentioning

confidence: 99%

A study on VLSI implementation of image enhancement techniques

Aklak

Pugazhenthi²,

Alex

2021

Concurrency and Computation

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In real-time, the images captured are not always appreciable for visual perception.The quality of the image relies on the illumination condition. Contrast enhancement is required to improve the visual quality of the image. In this article, the image enhancement techniques that are adaptable for real-time images are studied. Further, an investigation is done on implementing the image processing algorithms in very large-scale integration (VLSI) hardware. As the image data are comparatively larger, the computational complexity and the memory requirement are more. From the study, the need for efficient computation of such image processing algorithms is realized. The efficient computation of image processing algorithms on hardware platforms, in particular on field programmable gate array (FPGA), is reviewed. As an experiment, the gamma-transformation technique used for image enhancement is realized. Initially, the prototype is modeled using MATLAB, and then it is implemented using an FPGA. K E Y W O R D Sfield programmable gate array, floating point multiplier, image enhancement INTRODUCTIONIn the modern digital world, all multimedia transactions require good-quality images. The visibility of an image depends on the illuminance and reflectance of the scene in the real world. The brightness of a point in an image is non-linear to the brightness of the same point in the real world.Generally, lighting condition determines the quality of an image. The images taken under poor lighting conditions may cause discomfort while viewing images.The image acquired can be degraded due to various reasons such as sensor level malfunction, low visibility, excessive exposure, foggy conditions, and so on. 1 To overcome the degradation, pre-processing is required to enhance the image. Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further processing. It includes processes like deblurring, noise removal, filtering, and so on.Not all images will require the same image enhancement technique because there may be over-exposed regions in outdoor images that will need different treatment compared to indoor images. In literature, there are many algorithms proposed for image enhancement. The technique of being applied for enhancement depends on the input quality of the image. The quality needs to be assessed before applying any approach for a better perception of the image. A real-time implementation of image enhancement is hard to design using software because a large data set is required to represent an image. Since field programmable gate arrays (FPGAs) could exploit the spatial parallelism in image enhancement algorithms they are widely used for hardware implementation.Realizing the design on FPGAs, offer a compromise between the flexibility of general-purpose processors and the hardware-based speed of an integrated-circuit design. 2 Also, the programmable property of FPGA makes them a good choice for image processing applications, as the processing of the number of images also...

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