A Gradient-Based Spatial Color Algorithm for Image Contrast Enhancement

Lecca, Michela

doi:10.1007/978-3-030-30645-8_9

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…To address these challenges, more complex algorithms have been developed, such as adaptive contrast enhancements [4], [5], adaptive histogram equalization [6], [7], and Spatial Color Algorithms (SCA) [8], [9]. SCAs are driven by Retinex principles, inspired to the behavior of the Human Visual System (HVS).…”

Section: Introductionmentioning

confidence: 99%

An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications

et al. 2022

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Random Spray Retinex (RSR) is an effective image enhancement algorithm due to its effectiveness in improving image quality. However, the algorithm computing complexity and the required hardware resources and memory accesses hampered its deployment in many applications scenarios, for instance in IoT systems with limited hardware resources. With the rise of Artificial Intelligence (AI), the use of image enhancement has become essential to improve the performance for many emerging applications. In this paper, we propose the use of the RSR as a pre-processing filter before the task of semantic segmentation of low-quality urban road scenes. Using the publicly available Cityscapes dataset, we compare the performance of a pre-trained deep semantic segmentation network on dark noisy images and on RSR pre-processed images. Our findings confirm the effectiveness of RSR in improving segmentation accuracy. In addition, to address the computation complexity and suitability to edge devices, we propose a novel efficient implementation of the RSR using resistive random access memory (RRAM) technology. The architecture provides highly parallel analog in-memory computing (IMC) capabilities. A detailed, efficient and low latency implementation of the RSR using RRAM-CMOS technology is described. The design is verified using SPICE simulations with measured data from fabricated RRAM and 65nm CMOS technologies. The approach provided here represents an important step towards low-complexity and realtime hardware-friendly architecture and design for Retinex algorithms for edge devices.

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Section: Introductionmentioning

confidence: 99%

An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications

et al. 2022

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A Review on Designing of Memory Computing Architecture for Image Enhancement in AI Applications

Radhika

Ganesh

Babu³

2023

Lecture Notes in Computer Science

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Generalized equation for real-world image enhancement by Milano Retinex family

Lecca

2020

J. Opt. Soc. Am. A

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Milano Retinexes are spatial color algorithms grounded on the Retinex theory and widely applied to enhance the visual content of real-world color images. In this framework, they process the color channels of the input image independently and re-scale channel by channel the intensity of each pixel p by the so-called local reference white, i.e., a strictly positive value, computed by reworking a set of features sampled around p . The neighborhood of p to be sampled, its sampling, the features to be processed, as well as the mathematical model for the computation of the local reference white vary from algorithm to algorithm, determining different levels of enhancement. Based on the analysis of a group of Milano Retinexes, this work proves that the Milano Retinex local reference whites can be expressed by a generalized equation whose parameters model specific aspects of the Milano Retinex spatial color processing. In particular, tuning these parameters leads to different Milano Retinex implementations. This study contributes to a better understanding of the similarities and differences among the members of the Milano Retinex family, and provides new taxonomic schemes of them based on their own mathematical properties.

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A Gradient-Based Spatial Color Algorithm for Image Contrast Enhancement

Cited by 3 publications

References 21 publications

An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications

An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications

A Review on Designing of Memory Computing Architecture for Image Enhancement in AI Applications

Generalized equation for real-world image enhancement by Milano Retinex family

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