Dr. Kishor P. Upla Profile

Dr. Kishor P. Upla

at Sardar Vallabhbhai National Institute of Technology Surat

SPIE Involvement:

Author

Publications (5)

SPIE Journal Paper | 1 July 2021

ThermISRnet: an efficient thermal image super-resolution network

Heena Patel, Vishal Chudasama, Kalpesh Prajapati, Kishor Upla, Kiran Raja, Raghavendra Ramachandra, Christoph Busch

OE, Vol. 60, Issue 07, 073101, (July 2021) https://doi.org/10.1117/12.10.1117/1.OE.60.7.073101

KEYWORDS: Thermography, Lawrencium, Super resolution, Forward looking infrared, Thermal modeling, Cameras, Optical engineering, RGB color model, Data modeling, Network architectures

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SPIE Journal Paper | 18 January 2021

DepthFuseNet: an approach for fusion of thermal and visible images using a convolutional neural network

Heena Patel, Kishor Upla

OE, Vol. 60, Issue 01, 013104, (January 2021) https://doi.org/10.1117/12.10.1117/1.OE.60.1.013104

KEYWORDS: Image fusion, Computer programming, Thermography, Optical engineering, Image processing, Thermal modeling, Convolution, Visible radiation, Single crystal X-ray diffraction, Image quality

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SPIE Journal Paper | 12 January 2021

Computationally efficient progressive approach for single-image super-resolution using generative adversarial network

Vishal Chudasama, Kishor Upla

JEI, Vol. 30, Issue 02, 021003, (January 2021) https://doi.org/10.1117/12.10.1117/1.JEI.30.2.021003

KEYWORDS: Lawrencium, Super resolution, Performance modeling, Gallium nitride, Data modeling, Convolution, Image quality, Visualization, Image enhancement, Network architectures

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SPIE Journal Paper | 1 June 2018

Consistent pan-sharpening based on multistage joint and dual bilateral filters

Sharad Joshi, Kishor Upla, Nitin Khanna

JARS, Vol. 12, Issue 02, 026023, (June 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.026023

KEYWORDS: Image fusion, Satellites, Image processing, Image filtering, Satellite imaging, Spatial resolution, Distortion, Image quality, Spectral resolution, Remote sensing

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A pan-sharpening method using joint and dual bilateral filters (DBFs) has been proposed. This approach is based on a consistent combination of large- and small-scale features obtained from the decomposition of high spectral resolution multispectral (MS) and high spatial resolution panchromatic (PAN) images. In the decomposition process, MS and PAN images are used to extract the features using joint and DBFs, respectively. These filters accommodate the relationship between MS and PAN images and decompose them into a base layer (large-scale) and a detail layer (small-scale). Since the joint bilateral filter (JBF) preserves the edges of an auxiliary image, it is used for decomposition of MS images where different layers are estimated using the PAN image as an auxiliary image. Similarly, different layers of the PAN image are obtained from a DBF which preserves the edges of both (MS and PAN) input images. This process is further extended to multistage decomposition to obtain a bilateral image pyramid. The base and detail layers of both MS and PAN images obtained at various stages are combined using a weighted sum. Finally, the estimated weighted sum of detail layer (small-scale) of the PAN image is fused separately to the weighted base layers (large-scale) of the MS images. Performance of the proposed method is evaluated by conducting the experiments on degraded as well as undegraded datasets, captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The noise rejection capabilities of the proposed method are also tested by conducting experiments on the noisy data. The results are compared with the widely popular methods and the recently proposed fusion approaches based on a bilateral filter. Along with qualitative evaluation, the quantitative performance of the proposed fusion technique has also been verified by estimating different measures for degraded and undegraded experiments. The experimental results and quantitative measures demonstrate that the proposed method performs better in degraded and undegraded conditions along with noisy situations when compared to other state-of-art methods.

SPIE Journal Paper | 24 July 2015

Multiresolution image fusion using edge-preserving filters

Kishor Upla, Sharad Joshi, Manjunath Joshi, Prakash Gajjar

JARS, Vol. 9, Issue 01, 096025, (July 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096025

KEYWORDS: Image fusion, Image filtering, Satellites, Satellite imaging, Image quality, Spatial resolution, Feature extraction, Image processing, Earth observing sensors, Distortion

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We propose two approaches of multiresolution image fusion using multistage guided filter and difference of Gaussians (DoGs). In a multiresolution image fusion problem, the given multispectral (MS) and panchromatic (Pan) images have high spectral and high spatial resolutions, respectively. One can obtain the fused image using these two images by injecting the missing high frequency details from the Pan image into the MS image. The quality of the final fused image will then depend on the method used for high frequency details extraction and also on the technique for injecting these details into the MS image. Specifically, we have chosen the guided filter and DoGs for detail extraction since these are more versatile in applications involving feature extraction, denoising, and so on. The detail extraction process in the fusion approach using a guided filter exploits the relationship between the Pan and MS images by utilizing one of them as a guidance image while extracting details from the other. The final fused image is obtained by adding the extracted high frequency details to the corresponding MS image. This way, the spatial distortion of the MS image is reduced by consistently combining the details obtained using both MS and Pan images. In the fusion method using DoGs, the high frequency details are extracted in the first and second levels by subtracting the blurred images of the original Pan. The extracted details at both DoGs are added to the MS image to obtain the final fused image. Advantages and disadvantages of each method are discussed and the comparison of the results is shown between the two. The results are also compared with the traditional and the state-of-the-art methods using the images captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The quantitative assessment is evaluated using the conventional measures as well as using a relatively new index, i.e., quality with no reference which does not require a reference image. The results and measures clearly show that there is promising improvement in the quality of the fused image using the proposed approaches.

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