An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network

Abstract

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer’s global modeling ability and convolutional neural network’s local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing

“…Recently, visual transformers have also been utilized for image dehazing due to their capabilities for global modeling [20,[28][29][30][31]. To take the advantage of both visual transformers and CNNs, Xu et al [28] proposed a transformer-convolution fusion dehazing network.…”

“…Recently, visual transformers have also been utilized for image dehazing due to their capabilities for global modeling [20,[28][29][30][31]. To take the advantage of both visual transformers and CNNs, Xu et al [28] proposed a transformer-convolution fusion dehazing network. Guo et al [20] brought a haze density-related prior into the transformer via a novel transmission-aware 3D position-embedding module and modulated the CNN features via learning modulation matrices conditioned on transformer features, instead of simple feature addition or concatenation.…”

Universal Image Restoration with Text Prompt Diffusion

“…Given that there are multiple degradation types, single-task image restoration methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] involve training a single-task model for each type of degradation. While such an approach may yield favorable metrics for individual tasks, its applicability to complex real-world scenarios is challenging.…”

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

“…To protect geometric details while filtering, He et al further proposed the guided filtering theory [ 26 , 27 ], and some scholars have applied this theory to image haze removal [ 28 , 29 , 30 , 31 ]. The third type is based on the deep learning methods that have sprung up in recent years [ 32 , 33 , 34 , 35 , 36 , 37 ]. Cai et al used convolution neural network (CNN) to estimate the transmittance rate for the first time, and combined this with a traditional algorithm to remove haze from the image [ 33 ].…”