Point‐based spatial colour sampling in Milano‐Retinex: a survey

Lecca, Michela; Simone, Gabriele; Bonanomi, Cristian; Rizzi, Alessandro

doi:10.1049/iet-ipr.2017.1224

Cited by 15 publications

(4 citation statements)

References 37 publications

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“…We add that the purpose of this subsection was not carrying on a comprehensive comparison of the performance of the RSR variants in terms of enhancement of the image quality: similar studies can be found in [27], [51] and in several papers devoted to variants of RSR such as [52]-[55].…”

Section: B Retinex With Semantic Segmentationmentioning

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: B Retinex With Semantic Segmentationmentioning

confidence: 99%

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

et al. 2022

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“…The term readability here refers to the human understanding of the observed scene and of its details. We model this readability by five popular image features, most of which already employed in computer vision to assess the performance of an image enhancer (e.g., [21] , [19] ): the brightness, the contrast, the color distribution entropy, the local color variations of each frame, and perceptual intra-frame color changes.…”

Section: Introductionmentioning

confidence: 99%

A cockpit of multiple measures for assessing film restoration quality

Casiraghi

Lecca

Plutino

et al. 2020

Pattern Recognition Letters

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“…1062-3701/2019/63(6)/060403/6/$25.00 [1,2]. The idea has been successful and has generated a wide set of implementations for the original method [5][6][7] and for the many versions that arose from the later simplified version by Land [8,9]. A detailed description of Retinex history and of the differences among the many variants can be found in [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…The main characteristic of Retinex models is the spatial recomputation of each pixel value performed with a series of ratio operation in luminous intensity difference across the image. This idea of exploring the image content through paths is a common feature for many Retinex implementations (not all) and has been modeling studies [7,[13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Random Spray Retinex Extensions Considering Region of Interest and Eye Movements

Tanaka¹,

Lanaro²,

Horiuchi³

et al. 2019

jist

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The Random spray Retinex (RSR) algorithm was developed by taking into consideration the mathematical description of Milano-Retinex. The RSR substituted random paths with random sprays. Mimicking some characteristics of the human visual system (HVS), this article proposes two variants of RSR adding a mechanism of region of interest (ROI). In the first proposed model, a cone distribution based on anatomical data is considered as ROI. In the second model, the visual resolution depending on the visual field based on the knowledge of visual information processing is considered as ROI. We have measured actual eye movements using an eye-tracking system. By using the eye-tracking data, we have simulated the HVS using test images. Results show an interesting qualitative computation of the appearance of the processed area around real gaze points.

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Point‐based spatial colour sampling in Milano‐Retinex: a survey

Cited by 15 publications

References 37 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 cockpit of multiple measures for assessing film restoration quality

Random Spray Retinex Extensions Considering Region of Interest and Eye Movements

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