CKM VIGIL Private Limited
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Phone: (+91) 9381187495
Email: contact@ckmvigil.in
Website: https://ckmvigil.in/

Activity Results Report:"Study of Deep Learning algorithms for adverse weather
conditions"
Cloud classification and Segmentation:a novel deep learning model named ACLNet, for cloud segmentation from groundimages. ACLNet uses both deep neural network and machine learning (ML)algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as
the backbone, “atrous spatial pyramid pooling” (ASPP) to learn at multiple receptive
fields, and “global attention module” (GAM) to extract fine-grained details from theimage. ACLNet also uses K-means clustering to extract cloud boundaries more
precisely. ACLNet is effective for both daytime and nighttime images. It provides
lower error rate, higher recall, and F1-score predictive performance than state-of-art
cloud segmentation models.
ACLNet has a model size of ∼31MB and requires only 7.57 million floating-point
operations. Further, ACLNet has lowest error rate, higher recall and F1-score thanthe state-of-art models, for daytime, nighttime, and day+nighttime images(corresponding to SWIMSEG, SWINSEG and SWINySEG datasets, respectively).
The main contributions are:
• An interpretable and accurate framework targeted for cloud segmentation fromboth daytime and nighttime images.
• We introduce an ML algorithm in our DNN to bring their best together and more
effectively segment the clouds.

一 Left: ACLNet architecture (k denotes kernel size). Right: GAM module

Scene analysis in adverse weather conditions- Image dehazing:
Dehazing refers to removing the haze and restoring the details from hazy images.
ClarifyNet, a novel, end-to-end trainable, convolutional neural network architecture
for single image dehazing. We comprehensively evaluate ClarifyNet on I-HAZE, OHAZE, Dense-Haze, and NH-HAZE datasets using PSNR and SSIM metrics andcompare them with previous works. On Dense-Haze and NH-HAZE datasets,ClarifyNet achieves the highest PSNR and SSIM. For I-HAZE and O-HAZE,
ClarifyNet achieves the highest PSNR and remains within the top three techniqueson the SSIM metric.
Contributions:
1. In this paper, we propose ClarifyNet, a novel, end-to-end trainable, CNN
architecture for single image dehazing. ClarifyNet is a single-encoder, multidecoder attention-based network. The encoder uses EfficientNet-B6
backbone, which is pre-trained on ImageNet to extract image features.
ClarifyNet network uses several architectural primitives such as attention andsqueeze-and-excitation blocks to achieve high predictive performance.
2. We observe that a high-pass filter detects sharp edges, texture, and other finedetails present in the image, and a low-pass filter detects color and contrast
information. Based on this, our key idea and novelty is to train our network on
not only the haze-free ground-truth image but also on the “low-pass filtered”
and the “high-pass filtered” images. Thus, ClarifyNet performs dehazing byfocusing on color, edge, contrast, texture information and other fine details
present in the image. This leads to high reconstruction quality. The decoder
has three branches corresponding to the “low-pass filtered” image, “high-passfiltered” image and haze-free image. For each decoder branch, the sum of L1
loss and SSIM loss (LSSIM) is computed, and summation of this value for all
three branches is used as the final loss function for training ClarifyNet. Since
L1 loss and LSSIM loss are complementary, the use of both helps preserveboth pixel-level and structural similarity.
3. We evaluate ClarifyNet on four well-known datasets: I-HAZE, O-HAZE,Dense-Haze, and NH-HAZE. We compare our results with several recent
techniques on two metrics, namely peak signal to noise ratio (PSNR) andstructural similarity (SSIM). We report the results on both RGB color formatand YCbCr color format since they provide the best results on different
datasets. On Dense-Haze and NH-HAZE datasets, ClarifyNet achieves thehighest PSNR and SSIM. For I-HAZE and O-HAZE, ClarifyNet achieves thehighest PSNR and remains within the top three techniques on the SSIM metric.
Also, for dehazing a 3996×4096 image on a NVIDIA GeForce RTX 2080Ti
GPU, ClarifyNet takes only 1.5 seconds. Further, ClarifyNet has a model sizeof only 71 MB.

二 (a) Overall architecture of ClarifyNet (b) Decoder block (SE = squeeze and excitation) (c) Attention block

Problem/constraints:•Due to COVID-9 we were unable to procure nowcasting dataset, so we have exhaustively
worked on cloud classification/segmentation and image dehazing.

Research Output:••

ACLNet: An Attention and Clustering-based Cloud Segmentation Network- Accepted,
Remote Sensing Letters, Taylor & Francis Online, 2.583 (2020) Impact Factor (DOI
Assigned: https://doi.org/10.1080/2150704X.2022.2097031)
ClarifyNet: A High-Pass and Low-Pass Filtering Based CNN for Single Image DehazingUnder review, Journal of Systems Architecture, Science Direct, 3.77 Impact Factor

Human Capital Development:• Two PhD Students• Five undergraduate students