Iterative Convolutional Neural Network-Based Illumination Estimation

Koščević, Karlo; Subašić, Marko; Lončarić, Sven

doi:10.1109/access.2021.3057072

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…As a result, the proposed architecture makes meaningful improvements in estimation accuracy. Figures 4 and 5 The next experiments use several standard datasets, the Cube+, Gray-ball, and Multi-Cam datasets, to compare the performance of the proposed ARiRTN architecture against its most advanced counterparts [42][43][44][45][46][47][48][49][50][51]. In recent decades, the CNN architecture has played an integral role in performing advanced computer vision tasks, including regressing illuminants.…”

Section: Experimental Results and Evaluationsmentioning

confidence: 99%

ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination

Choi,

Kim

2023

Sensors

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In computational color constancy, regressing illumination is one of the most common approaches to manifesting the original color appearance of an object in a real-life scene. However, this approach struggles with the challenge of accuracy arising from label vagueness, which is caused by unknown light sources, different reflection characteristics of scene objects, and extrinsic factors such as various types of imaging sensors. This article introduces a novel learning-based estimation model, an aggregate residual-in-residual transformation network (ARiRTN) architecture, by combining the inception model with the residual network and embedding residual networks into a residual network. The proposed model has two parts: the feature-map group and the ARiRTN operator. In the ARiRTN operator, all splits perform transformations simultaneously, and the resulting outputs are concatenated into their respective cardinal groups. Moreover, the proposed architecture is designed to develop multiple homogeneous branches for high cardinality, and an increased size of a set of transformations, which extends the network in width and in length. As a result of experimenting with the four most popular datasets in the field, the proposed architecture makes a compelling case that complexity increases accuracy. In other words, the combination of the two complicated networks, residual and inception networks, helps reduce overfitting, gradient distortion, and vanishing problems, and thereby contributes to improving accuracy. Our experimental results demonstrate this model’s outperformance over its most advanced counterparts in terms of accuracy, as well as the robustness of illuminant invariance and camera invariance.

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Section: Experimental Results and Evaluationsmentioning

confidence: 99%

ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination

Choi,

Kim

2023

Sensors

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“…The proposed RiR-SDN architecture performs with much higher accuracy than basic convolution. The following experiments compare the proposed RiR-SDN architecture versus its latest counterparts [42][43][44][45][46][47][48][49][50][51][52] with several standard datasets: Cube+, Gray-ball, and Multi-Cam datasets. The last several decades have seen extensive CVCC models proposed in the CVCC community.…”

Section: Experimental Results and Evaluationsmentioning

confidence: 99%

“…The last several decades have seen extensive CVCC models proposed in the CVCC community. Despite significant advances in estimating illuminant color cast, there The following experiments compare the proposed RiR-SDN architecture versus its latest counterparts [42][43][44][45][46][47][48][49][50][51][52] with several standard datasets: Cube+, Gray-ball, and MultiCam datasets. The last several decades have seen extensive CVCC models proposed in the CVCC community.…”

Section: Experimental Results and Evaluationsmentioning

confidence: 99%

CVCC Model: Learning-Based Computer Vision Color Constancy with RiR-DSN Architecture

Choi

2023

Sensors

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To achieve computer vision color constancy (CVCC), it is vital but challenging to estimate scene illumination from a digital image, which distorts the true color of an object. Estimating illumination as accurately as possible is fundamental to improving the quality of the image processing pipeline. CVCC has a long history of research and has significantly advanced, but it has yet to overcome some limitations such as algorithm failure or accuracy decreasing under unusual circumstances. To cope with some of the bottlenecks, this article presents a novel CVCC approach that introduces a residual-in-residual dense selective kernel network (RiR-DSN). As its name implies, it has a residual network in a residual network (RiR) and the RiR houses a dense selective kernel network (DSN). A DSN is composed of selective kernel convolutional blocks (SKCBs). The SKCBs, or neurons herein, are interconnected in a feed-forward fashion. Every neuron receives input from all its preceding neurons and feeds the feature maps into all its subsequent neurons, which is how information flows in the proposed architecture. In addition, the architecture has incorporated a dynamic selection mechanism into each neuron to ensure that the neuron can modulate filter kernel sizes depending on varying intensities of stimuli. In a nutshell, the proposed RiR-DSN architecture features neurons called SKCBs and a residual block in a residual block, which brings several benefits such as alleviation of the vanishing gradients, enhancement of feature propagation, promotion of the reuse of features, modulation of receptive filter sizes depending on varying intensities of stimuli, and a dramatic drop in the number of parameters. Experimental results highlight that the RiR-DSN architecture performs well above its state-of-the-art counterparts, as well as proving to be camera- and illuminant-invariant.

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“…In [16], the authors propose a very deep model for illuminant estimation (CRNA) that uses cascading residual connections and ResNet architecture to stabilize learning and improve performance. Similarly, in [17], the authors propose a deep network which iteratively estimates the illumination, which is also used to stabilize training and improve performance. On the other hand, in [18], a small network that still achieves state-of-the-art results for illuminant estimation is proposed.…”

Section: Related Workmentioning

confidence: 99%

Framework for Illumination Estimation and Segmentation in Multi-Illuminant Scenes

et al. 2023

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Color constancy is an important part of the human visual system, as it allows us to perceive the colors of objects invariant to the color of the illumination that is illuminating them. Modern digital cameras have to be able to recreate this property computationally. However, this is not a simple task, as the response of each pixel on the camera sensor is the product of the combination of spectral characteristics of the illumination, object, and the sensor. Therefore, many assumptions have to be made to approximately solve this problem. One common procedure was to assume only one global source of illumination. However, this assumption is often broken in real-world scenes. Thus, multi-illuminant estimation and segmentation is still a mostly unsolved problem. In this paper, we address this problem by proposing a novel framework capable of estimating per-pixel illumination of any scene with two sources of illumination. The framework consists of a deep-learning model capable of segmenting an image into regions with uniform illumination and models capable of single-illuminant estimation. First, a global estimation of the illumination is produced, and is used as input to the segmentation model along with the original image, which segments the image into regions where that illuminant is dominant. The output of the segmentation is used to mask the input and the masked images are given to the estimation models, which produce the final estimation of the illuminations. The models comprising the framework are first trained separately, then combined and fine-tuned jointly. This allows us to utilize well researched single-illuminant estimation models in a multi-illuminant scenario. We show that such an approach improves both segmentation and estimation capabilities. We tested different configurations of the proposed framework against other single-and multi-illuminant estimation and segmentation models on a large dataset of multi-illuminant images. On this dataset, the proposed framework achieves the best results, in both multi-illumination estimation and segmentation problems. Furthermore, generalization properties of the framework were tested on often used single-illuminant datasets. There, it achieved comparable performance with state-of-the-art single-illumination models, even though it was trained only on the multi-illuminant images.

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Iterative Convolutional Neural Network-Based Illumination Estimation

Cited by 8 publications

References 42 publications

ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination

ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination

CVCC Model: Learning-Based Computer Vision Color Constancy with RiR-DSN Architecture

Framework for Illumination Estimation and Segmentation in Multi-Illuminant Scenes

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