An encoder-decoder based deep architecture for visible to near infrared image transformation

Aswatha, Shashaank M.; Malladi, Sai Phani Kumar; Mukherjee, Jayanta

doi:10.1145/3490035.3490288

Cited by 5 publications

(9 citation statements)

References 25 publications

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“…If IR and depth images can be estimated from an RGB image, a lip-reading system with a high recognition rate can be implemented by using existing RGB cameras without adding/changing any hardware. Previous studies demonstrated the feasibility of using RGB images to predict a different domain for its application-specific representation [44]. Accordingly, in this paper, we propose a method to improve the accuracy of lip-reading by using IR/depth images estimated from RGB images instead of the actually captured IR/depth images.…”

Section: Prediction Of Ir/depth Images Using An Optical Rgb Imagementioning

confidence: 99%

“…Despite the shared information between the two images, each image has unique characteristics that cannot be explained by simple dependency relationships. Therefore, the correspondence between the two images was primarily represented by nonlinear mapping rules, such as deep neural networks [38][39][40][42][43][44][45]49,53]. Typically, a CNN was adopted to estimate the IR/depth images from optical RGB images.…”

Section: Prediction Of Ir/depth Images Using An Optical Rgb Imagementioning

confidence: 99%

“…The architecture of the CNN used in this study was similar to that of U-net [53], as illustrated in Figure 4. In a previous study, a dual encoderdecoder-based architecture with different depths [44] and conditional generative adversarial networks [45,49] was employed to estimate IR images from optical RGB images. We performed evaluation tests on these two forms of architecture from a recognition accuracy perspective.…”

Section: Prediction Of Ir/depth Images Using An Optical Rgb Imagementioning

confidence: 99%

“…An attempt was also made to use RGB images to predict IR images for use in visualizing vegetation mapping [44][45][46][47] and vein patterns [48][49][50]. In order to improve the accuracy of lip-reading under different lighting conditions, RGB-to-IR image conversion was also adopted in this study.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Performance of Automatic Lip-Reading Using Image Conversion Techniques

Lee

2024

Electronics

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Variation in lighting conditions is a major cause of performance degradation in pattern recognition when using optical imaging. In this study, infrared (IR) and depth images were considered as possible robust alternatives against variations in illumination, particularly for improving the performance of automatic lip-reading. The variations due to lighting conditions were quantitatively analyzed for optical, IR, and depth images. Then, deep neural network (DNN)-based lip-reading rules were built for each image modality. Speech recognition techniques based on IR or depth imaging required an additional light source that emitted light in the IR range, along with a special camera. To mitigate this problem, we propose a method that does not use an IR/depth image directly, but instead estimates images based on the optical RGB image. To this end, a modified U-net was adopted to estimate the IR/depth image from an optical RGB image. The results show that the IR and depth images were rarely affected by the lighting conditions. The recognition rates for the optical, IR, and depth images were 48.29%, 95.76%, and 92.34%, respectively, under various lighting conditions. Using the estimated IR and depth images, the recognition rates were 89.35% and 80.42%, respectively. This was significantly higher than for the optical RGB images.

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Section: Prediction Of Ir/depth Images Using An Optical Rgb Imagementioning

confidence: 99%

Section: Prediction Of Ir/depth Images Using An Optical Rgb Imagementioning

confidence: 99%

Section: Prediction Of Ir/depth Images Using An Optical Rgb Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving the Performance of Automatic Lip-Reading Using Image Conversion Techniques

Lee

2024

Electronics

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“…Furthermore, they improve the quality of generated thermal images by integrating gradient and intensity losses, which helps the generator learn better characteristics of the thermal images. [77] propose a visible to Near-Infrared (NIR) image translation model based on GANs. Their proposed model consists of two UNet models as generators, one for the regular image, and the other one for a lower resolution version of the image.…”

Section: Related Workmentioning

confidence: 99%

Thermal image analytics for industrial anomaly detection

Goyal

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I extend my deepest appreciation to my mother and brother, whose unwavering support, motivation, and presence have been a constant source of strength throughout this academic endeavour. Their encouragement and belief in my capabilities have played a pivotal role in overcoming challenges and reaching milestones. I am also grateful for the enduring inspiration provided by my father, whose legacy continues to guide me in all aspects of life. His influence has been a beacon of inspiration, and I dedicate this achievement to his memory.

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A feature refinement and adaptive generative adversarial network for thermal infrared image colorization

Chen,

Zhan,

Jiang

et al. 2024

Neural Networks

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An encoder-decoder based deep architecture for visible to near infrared image transformation

Cited by 5 publications

References 25 publications

Improving the Performance of Automatic Lip-Reading Using Image Conversion Techniques

Improving the Performance of Automatic Lip-Reading Using Image Conversion Techniques

Thermal image analytics for industrial anomaly detection

A feature refinement and adaptive generative adversarial network for thermal infrared image colorization

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