ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing

作     者：Susladkar, Onkar Deshmukh, Gayatri Nag, Subhrajit Mantravadi, Ananya Makwana, Dhruv Ravichandran, Sujitha Teja, R. Sai Chandra Chavhan, Gajanan H. Mohan, C. Krishna Mittal, Sparsh 

作者机构：Vishwakarma Inst Informat Technol Pune India IIT Hyderabad Hyderabad India IIIT Raichur Raichur India CKM Vigil Pvt Ltd Hyderabad India NIT Trichy Trichy India IIT Roorkee Roorkee India 

出 版 物：《JOURNAL OF SYSTEMS ARCHITECTURE》 (系统结构杂志)

年 卷 期：2022年第132卷第0期

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：WNI WxBunka Foundation, Japan IIT Roorkee, India [FIG-100874] 

主　　题：Single-image dehazing Convolutional neural network Encoder-decoder architecture Attention Low-pass filter High-pass filter 

摘      要：Dehazing refers to removing the haze and restoring the details from hazy images. In this paper, we propose ClarifyNet, a novel, end-to-end trainable, convolutional neural network architecture for single image dehazing. We note that a high-pass filter detects sharp edges, texture, and other fine details in the image, whereas a low-pass filter detects color and contrast information. Based on this observation, our key idea is to train ClarifyNet on ground-truth haze-free images, low-pass filtered images, and high-pass filtered images. Based on this observation, we present a shared-encoder multi-decoder model ClarifyNet which employs interconnected parallelization. While training, ground-truth haze-free images, low-pass filtered images, and high-pass filtered images undergo multi-stage filter fusion and attention. By utilizing a weighted loss function composed of SSIM loss and L1 loss, we extract and propagate complementary features. We comprehensively evaluate ClarifyNet on I-HAZE, O-HAZE, Dense-Haze, NH-HAZE, SOTS-Indoor, SOTS-Outdoor, HSTS, and Middlebury datasets. We use PSNR and SSIM metrics and compare the results with previous works. For most datasets, ClarifyNet provides the highest scores. On using EfficientNet-B6 as the backbone, ClarifyNet has 18 M parameters (model size of similar to 71 MB) and a throughput of 8 frames-per-second while processing images of size 2048 x 1024.